MIXED CROP LIVESTOCK SYSTEMS AND CONSERVATION TILLAGE: FARM PROFITABILITY, ADOPTION POTENTIAL, AND ENVIRONMENTAL IMPACTS By MARCELA QUINTERO A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN P ARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2014
Â© 2014 Marcela Quintero
To my son, husband, parents and mentors
4 ACKNOWLEDGMENTS I am very grat North Florida Research and Education Center (UF NFREC), for his constant support and patience during my doctorate studies and research work. I also appreciate his flexibility in allowin g me to base myself in Florida or Colombia, according to the emerging research activities in the two study sites. His openness and support have permitted me to develop and propose the methods used in this dissertation. I also wish to thank the other member s of my advisor committee: Dr. Nicholas Comerfor d; Dr. James Marois; and Dr. Al an Hodges; and Dr. Jason Ferrell for offering advice and suggestions whilst I prepared the research proposal and during its implementation. I would also like to thank the Intern ational Center for Tropical Agriculture (CIAT), for which I have worked since 2002 and that has given me the flexibility required to pursue this doctorate degree. Also, I would like to thank many other people whose help was important in the implementation of this research. To the social scientists Wendy lin Bartels and Michelle extend my gratitude. Also to various anonymous farmers who, during a field day at the UF NFREC, re sponded to the first version of the survey; this was of great help to making useful adjustments. To all the administrative staff at UF NFREC, who provided valuable help when I was mailing the survey out to several farmers in the region. I also wish to than k Paula Posada and Fredy Monserrate from CIAT, and Wilson Otero and his team from FUNDESOT who helped in the field implementation and management of the field experiments in Colombia from 2011 to 2013. To Alex Buritica, statistician from CIAT, I offer my gr atitude for the guidance offered in the development of the empirical
5 models, which was useful in the adoption analysis conducted for the sod based rotation in Southeastern US. I also acknowledge the Consultative Group for International Agricultural Researc Environmental Authority in Colombia (CAR), which, through CIAT, provided the necessary financial support to fund the research experiment in Colombia. Also, I want to express my gratitude to St ephanie Lee who helped with the English editing and proof reading of my final dissertation. Finally, special gratitude goes to my family for be ing supportive during the development of my doctorate, special thanks to my husband and son who accepted giving u p family time to allow me to complete my studies.
6 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 9 LIST OF FIGURES ................................ ................................ ................................ ........ 11 LIST OF ABBREVIATIONS ................................ ................................ ........................... 13 ABSTRACT ................................ ................................ ................................ ................... 14 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 16 2 ADOPTION POTENTIAL OF SOD BASED ROTATIONS IN SOUTHEASTERN UNITED STATES ................................ ................................ ................................ ... 22 Overview of Research Problem ................................ ................................ .............. 22 Fac tors that influence the adoption of conservation practices in agriculture .... 24 Factors that may influence the adoption of SBR by farmers in Southeast US .. 29 Methods ................................ ................................ ................................ .................. 31 Results ................................ ................................ ................................ .................... 33 Sample descriptive characteristics ................................ ................................ ... 33 Reasons for planning to use or not sod based rotations ................................ .. 34 Factors influencing adoption of SBR ................................ ................................ 35 Discussio n ................................ ................................ ................................ .............. 37 Perceptions of farmers about SBR ................................ ................................ ... 40 Technology dissemination recommendations and opportunities ...................... 43 Summary ................................ ................................ ................................ ................ 44 3 EX ANTE IMPACT ASSESSMENT OF SOD BASED ROTATION ON PEANUT COTTON FARMS IN THE SOUTHEASTERN US ................................ .................. 53 Overview of Research Problem ................................ ................................ .............. 53 Methods ................................ ................................ ................................ .................. 57 Specification of the model ................................ ................................ ................ 57 Model parameters and crop rotation alternatives assessed ............................. 59 Constraints: ................................ ................................ ................................ ...... 59 Rotation alternatives and information sources: ................................ ................. 60 Alternatives for a peanut cotton grower: ................................ .................... 61 Alternatives for a peanut cotton grower that has cattle: ............................. 63 Results and Discussion ................................ ................................ ........................... 66 Economic benefits of sod based rotations for small peanut and cotton growers ................................ ................................ ................................ ......... 66
7 Economic benefits of sod based rotations for small farmers growing cotton and peanuts and raising cattle ................................ ................................ ...... 69 How attainable are net revenues increa ses through the adoption of SBR in the Southeastern US? ................................ ................................ ................... 71 Summary ................................ ................................ ................................ ................ 75 4 EFFECT OF CONSERVATION AND CONVENTIONAL TILLAGE ON RUNOF F AND SOIL, NITROGEN (N) AND PHOSPHORUS (P) LOSSES IN A POTATO BASED MIXED CROP LIVESTOCK SYSTEMS IN COLOMBIA ............................ 83 Overview of Research Problem ................................ ................................ .............. 83 Methods ................................ ................................ ................................ .................. 89 Study Site ................................ ................................ ................................ ......... 89 Field methods and experimental design ................................ ........................... 89 Lab methods ................................ ................................ ................................ ..... 92 Data analysis ................................ ................................ ................................ .... 92 Results ................................ ................................ ................................ .................... 93 Ra infall and runoff ................................ ................................ ............................ 93 Andosols ................................ ................................ ................................ ........... 93 Surface runoff volume and sediments ................................ ....................... 93 Concentration of nitrate N, ammonium N and phosphorus in runoff water and sediments ................................ ................................ ............... 94 Total nutrient losses in runoff water and eroded soil ................................ .. 95 Inceptisols ................................ ................................ ................................ ........ 97 Surface runoff volume and sediments ................................ ........................ 97 Concentration of nitrate N, ammonium N and phospho rus in runoff water and eroded soil ................................ ................................ ............. 98 Total nutrient losses in runoff water and eroded soil ................................ .. 99 Sampling event effect ................................ ................................ ....................... 99 Discussion ................................ ................................ ................................ ............ 100 Impacts of crops in nutrient concentration and losses in runoff water and sediments ................................ ................................ ................................ .... 100 Impact of the whole rotation on nutrient and sediment losses ........................ 110 Environmental implications ................................ ................................ ............. 111 Summary ................................ ................................ ................................ .............. 112 5 CONCLUSIONS ................................ ................................ ................................ ... 140 Sod based rotations with conservation tillage in Southeastern US ....................... 141 Sod based rotations with conservation tillage in Colombia ................................ ... 144 Further research needs ................................ ................................ ......................... 147 APPENDIX A LETTERS AND SURVEY USED TO ASSESS ADOPTION POTENTIAL OF SOD BASED ROTATIONS ................................ ................................ ................... 1 51
8 Prenotice letter ................................ ................................ ................................ ...... 151 r ................................ ................................ ............................. 152 Postcard reminder ................................ ................................ ................................ 158 B DESCRIPTION OF THE LINEAR PROGRAMMING MODEL ............................... 159 C SOIL PROFILES DESCRIPTION AND CHARACTERISTICS .............................. 161 D STRUCTURE FOR RUNOFF AND SOIL LOSS SAMPLING ................................ 163 LIST OF REFE RENCES ................................ ................................ ............................. 164 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 176
9 LIST OF TABLES Table page 2 1 Selected independe nt variables to explain SBR adoption ................................ .. 47 2 2 Survey responses ................................ ................................ ............................... 47 2 3 Descriptive statistics about the adoption potential of SBR ................................ .. 48 2 4 Descriptive statistics about potential and non potential adopters of SBR ........... 49 2 5 Primary motivation to practice SBR within the next 3 years ............................... 50 2 6 Primary reason that keeps farmers out from practicing SBR .............................. 50 2 7 Probit model of adoption of sod based r otation in Southeast US ....................... 51 2 8 ............................... 52 3 1 Rotations considered for the multi year linear programming modeling. ............... 77 3 2 Cotton and peanut yields used per land use scenario ................................ ........ 77 3 3 Total production costs and p rices for peanuts and cotton used in the analysis .. 79 3 4 Total production costs and prices for bahiagrass and cattle management used in the analysis ................................ ................................ ............................ 80 3 5 Economic benefits of conventional vs. sod based rotations alternatives for a peanut cotton grower ................................ ................................ .......................... 81 3 6 Economic benefits of conventional vs. sod based rotations a lternatives for a peanut cotton and livestock farmer ................................ ................................ ..... 82 4 1 Crop sequence and planting dates per type of rotation ................................ .... 115 4 2 Surfac e runoff volume per cover and crop cycle in study site 1 ........................ 115 4 3 Analysis of variance of runoff, soil and nutrient losses per sampling event ...... 116 4 4 Soil losses per cover and crop cycle in study site 1 ................................ .......... 117 4 5 Concentration of nutrients in runoff water in site 1 ................................ ............ 117 4 6 Concentration of nutrients in sediments in site 1 ................................ .............. 118 4 7 Surface runoff volume per cover and crop cycle in study site 2 ........................ 124
10 4 8 Soil losses per cover and crop cycle in study site 2 ................................ .......... 124 4 9 Concentration of nutrients in runoff water in site 2 ................................ ............ 125 4 10 Concentration of nutrients in sediments in site 2 ................................ .............. 125 B 1 Representation of the linear programming model used in this analysis ............ 160
11 LIST OF FIGURES Figure page 3 1 Rotation alternatives assessed. ................................ ................................ .......... 78 4 1 Total rainfall and average runoff per cover treatment and cro p cycle . .............. 114 4 2 Total nutrient losses in runoff water in site 1 (Andosols). ................................ . 119 4 3 Total nutrient losses in se diments in site 1 (Andosols) ................................ ..... 120 4 4 Total NH 4 N losses in sediments and runoff water in site 1 (Andosols). ........... 121 4 5 Total NO 3 N losses in sedime nts and runoff water in site 1 (Andosols). ........... 122 4 6 Total P losses in sediments and runoff water in site 1 (Andosols). ................... 123 4 7 To tal nutrient losses in runoff water in site 2 (Inceptisols) ................................ 126 4 8 Total nutrient losses in sediments in site 2 ( Inceptisols) ................................ ... 127 4 9 Total NH 4 N losses in sediments and runoff water in site 2 (Inceptisols). ......... 128 4 10 Total NO 3 N losses in sediments and runoff water in site 2 (Inceptisols) ......... 129 4 11 Total P losses in sediments and runoff water in site 2 (Inceptisols). ................. 130 4 12 Total nutrient losses in sediments and runoff water per rot ation type in site 1 (Andosols) ................................ ................................ ................................ ........ 131 4 13 Total nutrient losses in sediments and runoff water per rotation system in site 2 (Inceptis ols) ................................ ................................ ................................ ... 132 4 14 Average of the sum of total soil losses per rotation system .............................. 133 4 15 Average of the sum of total runoff water per rotation system ........................... 134 4 16 Nutrient concentration in runoff an d permissible limits (Andosols) ................... 135 4 17 Phosphorous concentrations in runoff and permissible limits according to recommended levels (Ando sols). ................................ ................................ ..... 136 4 19 Phosphorous concentrations in runoff and permissible limits (Inceptisols). ...... 138 4 20 Cover c rop residues in potat o cropping . ................................ ........................... 139 C 1 Soil profiles description and characteristics in site 1 ................................ ......... 161
12 C 2 Soil profiles description and characteristic s in site 2 (Inceptisol) ...................... 162 D 1 Galvanized metallic structure for runoff and soil loss sampling ........................ 163 D 2 Metallic structure for runoff and soil loss collection in different crops ............... 163
13 LIST OF ABBREVIATIONS ARMS Agricultural Resource Management Survey ANOVA Analysis of variance CAR Corporacion Autonoma Regional de Cundinamarca CIAT Internation al Center for Tropical Agriculture CT Conservation tillage ECOSAUT Model of optimization for ex ante evaluation of land use alternatives and environmental externalities EQIP Environmental Quality Incentives Program ERS Economic Research Service GI Z De utsche Gesellschaft fÃ¼r Internationale Zusammenarbeit IT Intensive tillage NASS National Agricultural Statistics Service UG CES US United States of America USDA United States Department of Agricult ure SBR Sod based rotation UF NFREC Center
14 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirement s for the Degree of Doctor of Philosophy MIXED CROP LIVESTOCK SYSTEMS AND CONSERVATION TILLAGE: FARM PROFITABILITY, ADOPTION POTENTIAL, AND ENVIRONMENTAL IMPACTS By Marcela Quintero August 2014 Chair: David Wright Cochair: Jason Ferrell Major: Agronomy This research focused on two types of mixed crop livestock systems: i) a sod based rotation (SBR) in Southeastern US that incorporates bahiagrass ( Paspalum n o tatum Fluegge) and conservation tillage (CT) in the conventional cotton peanut rotation; and ii) a potato pasture rotation ( Solanum tuberosum Lolium perenne ) in Colombia that incorporates CT to reduce water pollution with plant nutrients. The objectives of this study were: 1) to assess the adoption potential of SBR in Southeastern US; 2) to analyze rotation; and 3) to evaluate the effect of conservation and conventional tillage in the trial system in Colombia on nutrient losses and concentrations in runoff. For the first objective, response to surveys disseminated by mail, were used to build an empirical model to test what characteristics might be associated with willingness to adopt the SBR. Results indicate that 45% of respondents were willing to practice SBR and that the probability of a p ositive response of adoption is higher for farmers that: a) feed animals with products other than hay and grazed pastures; (b) first heard about SBR through an extension visit to their farm; (c) do not receive government
15 payments; (d) use intensive tillage methods; (e) are growing peanuts and cotton; (f) have farm sales per year in the range of US$50,000 $99,999; and (g) are under 50 years old. For the second objective, a linear programming model was used to determine the show that for peanut cotton producers, SBR can generate important increases in net revenues (over US$270,000 over ten years) relative to conventional rotations. When SBR are used by farmers who own cattle, the net revenue are increased compared to farming crops and cattle separately. The third objective achieved by performing a field experiment, between 2011 and 2013 found that impacts of CT vary in accordance with soil type and precipitation variability between cropping cycles. In general, CT reduced nutr ient losses in Inceptisols while it did not do so in Andosols. P concentrations in runoff water in all rotations and of n itrate N in potato with CT and oats were above the recommended limits.
16 CHAPTER 1 INTRODUCTION Since the green revolution, developed countries have transited toward the specialization of agriculture with the aim of significantly increasing productivity and economies of scale. As a result, their agricultural production has focused on those few crops for which any external input and the i nfrastructure would be optimized (de Wit, 1992). The overall outcome has been the establishment of less diversified agricultural systems than those prior to the green revolution (Gardner, 2002). In contrast, in developing countries of Asia, Africa and Lat in America, mixed crop livestock systems are responsible for most of the meat and milk produced in these countries; and are a response to increases in population density and the reduction in land available for agriculture (Thornton and Herrero, 2001). Most of these systems are managed by smallholders who benefit from more diversified crop production, which allows them to more efficiently use available natural and labor resources throughout the year (Alene et al., 2006). Despite the differences between these two types of agriculture, they both caused unintended environmental consequences and hence both models face challenges from an environmental conservation perspective. In specialized large agricultural systems, soils remain without vegetative coverage for extended periods of times; this renders them vulner able to water and wind erosion that affects the physical structure of soils and causes the loss of soil and nutrients via runoff and leaching (Duynisveid et al . , 1988). Furthermore, the diversity of farmin g systems has decreased and the fewer crops in these types of systems are increasingly affected by pests, diseases and weeds that are difficult to control . The use of industrial fertilizers, herbicides and pesticides has
17 increased in specialized agricultur al systems (de Wit, 1990). For their part, mixed crop livestock systems, although more diverse, can also cause negative environmental impacts if best management practices are not employed. This situation is evidenced in the Andean region of Latin America. For example, in the cultivation of crops that form part of crop pasture rotations in the hillsides of the Colombia Andes, intensive tillage implements (e.g. rotary tiller and disc plow) are commonly used . These implements invert the soil and disrupt its st ructure, which can then result in excessive soil loss and compaction (Leiva et al., 2002). Generalized negative environmental impacts of agriculture, in combination with the need to meet increasing global food demands, have led to a more prominent discour se in the policy making and academic sectors regarding the need to identify and shift toward more productive and sustainable forms of agriculture. Such discourse has crystallized in the so of production wherein productivity is increased while environmental impacts are reduced (Baulcombe et al., 2009; Garnett and Godfray, 2012). Although this concept was developed in the context of Africa where low yields and environmental degradation is a co ncern, this has been also endorsed by the farming industry in developed countries (Garnett and Godfray, 2012). Nonetheless, developments around this definition are aspi rational item on the agenda of numerous policy makers and development agencies and references to this new approach provide little guidance as to how agricultural systems should look like in its actual implementation (Garnett and Godfray,2012).
18 Garnet et a l. (2013) believe that putting the sustainable intensification concept into practice requires radically rethinking how food production might look like in order to achieve higher production and lower environmental impacts. For this, the scientific contributions will be vital for providing the appropriate means to design more sustainable and productive food systems that consider the socio economic and cultural conditions specific to each context, and provide reliable metrics to measure their performa nce (Garnett and Godfray, 2012). So, how might these systems look like in developed and developing countries? Despite the lack of details and consensus as to what food systems might be considered sustainable intensification alternatives and how these sho uld be evaluated from environmental and socioeconomics perspectives, there are, nonetheless, already some operational examples that are providing insights into how conventional systems can be redesigned so as to reduce environmental impacts while increasin g food production. These examples include improved practices for soil and water resource conservation such as reduced tillage, crop rotations, and cover crops, among others. However, the evaluation of new ly proposed systems will require long term effor ts t hat systematically analyze each aspect for performance in agronomic, environmental and economic terms, as well as evaluate the likelihood of adoption of such systems in specific contexts. In general, research efforts oriented toward evaluating the impacts of combining conservation tillage with crop pastures rotations demonstrate that this approach can deliver positive environmental impacts and maintain or even increase crop yields. This is the case for research conducted in two sod based rotations, the fir st carried out in a developed context and the second in a developing country. The first research effort is
19 which for the last decade has investigated the environmental a nd crop productivity benefits of a system that includes perennial grasses and conservation tillage in the traditional cotton peanut rotation in the Southeastern of US. The second research initiative is run jointly by the Colombian based: regional environme ntal authority (CAR); and the International Center for Tropical Agriculture (CIAT). This joint undertaking has sought to investigate the impacts of introducing conservation tillage practices in already mixed systems where potato cropping is rotated with pa stures. In the first research initiative in the Southeastern US, the sod based rotation (SBR) consists of bahiagrass ( Paspalum not atum Fluegge) grown for two years prior to row crops (i.e. cotton Gossypium hirsutum L. and peanut Arachis hypogaea L.) in a conservation tillage system in the following sequence: bahiagrass bahiagrass peanut cotton (Wright et al., 2012). Results from long term experiments performed in North Florida demonstrate that this sod based rotation achieves: reductions in nitrate leach ing; increases in soil organic matter; enhancement of water infiltration rates; and improvements in the soil fauna (Katsvairo et al. , 2006). From the productivity perspective, peanut and cotton yields are increased when grown in lands rotated with perennia l grasses (Marois and Wright, 2003; Katsvairo et al., 2006; Dickson and Hewlett, 1989; Elkins et al., 1977); similarly, in this mixed crop livestock system, the weights of cattle weight and stocking rates increased (George et al. , 2013). The Colombian cas e began in1999, when the Colombian regional environmental authority (CAR) in partnership with the Technical German Cooperation (GI Z) begun adapting and promoting the principles of conservation tillage in potato based systems
20 throughout the Andes. Since the n the CAR has continued supporting the adoption of these practices, via extension activities , in order to reduce water erosion and nutrient losses from these systems, whilst simultaneously improving potato yields and reducing soil preparation costs. Althou gh CAR dedicated its early efforts toward extension of this system in various Colombian Andean landscapes; in 2005 it teamed up with CIAT to support evaluation of the scale of the environmental and economic impacts of CT in potato based ( Solanum tuberosum ) mixed crop livestock systems in the Fuquene Lake watershed. Since then some environmental and economic impacts have been reported. Quintero (2009) found that conservation tillage rotations practiced in the upper part of the Fuquene watershed increased the net revenues of the farms by 17% compared to those generated by the conventional tillage rotations. Also, Quintero and Comerford (2013) reported that conservation tillage , when practiced in soils of the upper part of this watershed, increases the amount o f carbon stored in the soils compared to its status in conventional tillage systems. Although such impacts of these systems have been demonstrated, research questions remain, such as: what is the adoption potential of sod based rotations in Southeastern U S?; what are the long term economic benefits of the entire mixed system?; what is the impact of conservation tillage in Colombian potato pasture rotations on nutrient losses from the system that are causing the eutrophication of Fuquene Lake? Thus the obj ectives of this dissertation were: 1. to assess the adoption potential of sod based rotation in Southeastern US; 2. to analyze the economic benefits of sod based rotations and conventional peanut cotton rotation for North Florida farms already in the peanuts an d cotton business; and
21 3. to evaluate the effect of conservation and conventional tillage on soils, nitrogen (N) and phosphorus (P) losses and concentrations (caused by runoff) in potato based mixed crop livestock systems in Colombia. The achievement of thes e objectives contributes missing pieces of the puzzle of how these mixed crop livestock systems might contribute to the sustainable intensification of agriculture in their respective contexts . To address these objectives; C hapter 2 focuses on O bjective 1, which was addressed by conducting a (mailed) survey of a random sample of farmers in the Southeastern US who had already participated in sod based rotation extension activities. The purpose of the survey was to ented technology and their willingness to practice it in the short term; and the characteristics of the farm, farmer and technology associated with potential and non potential adopters. Chapter 3 addresses O bjective 2 and simulates the economics of both t he conventional and sod based rotations in their entirety for a 10 year period, in order to understand the marginal economic benefits of implementing sod based rotations in two different farm businesses. These distinct farm businesses comprise: i) farm s th at grows peanuts and cotton in a conventional rotation (without perennial grasses and conservation tillage); and ii) farm s with the same rotation but with cattle raised independently from crop cropping activities. Chapter 4 is devoted to the Colombian mixe d crop l ivestock systems and addresses O bjective 3. This explains the field experiment, performed between 2011 and 2013, to measure soil and nutrient losses and concentrations caused by water erosion (under natural rainfall conditions) in potato pasture ro tations both with, and without, conservation tillage conclusions and offers future research recommendations.
22 CHAPTER 2 ADOPTION POTENTIAL OF SOD BASED ROTATIONS IN SOUTHEASTER N UNITED STATES Overview of Research Problem I n the developed countries, the green revolution has brought about a necessary increase in production by optimizing the growing conditions where external inputs are used in such a way that the production possib ilities of all other available resources like land and water are fully exploited (de Wit, 1992). This, along with the economies of scale, has narrowed down the crop rotations to a few crops. However, this process has caused unintended consequences. Since s oils may be left bare for six months or longer, they are subjected to structural breakdown, nitrogen leaching (Duynisveid et al., 1988) and to wind and water erosion. Also, the diversity of farming systems has decreased and the increased use of industrial fertilizers appears to be detrimental to the environment. The few remaining crops are increasingly affected by pests, diseases and weeds that are difficult to control. Due to this situation and increases in oil and fertilizer prices, the agricultural rese arch sector is interested in finding new agriculture management practices that may reverse these negative consequences of agricultural specialization, increase competitiveness of the smaller farmers and utilize land resources more efficiently . Options like widening crop rotations, developing soil cultivation practices and machinery that conserve the structure of the soil, introducing crops and crop combinations that keep the soil covered for longer periods of the year (de Wit, 1990) and the combination of l ivestock and crops that use and recycle available nutrients in a more efficient way are being investigated.
23 Center has been investigating the environmental and crop productivity benefits of a system that includes perennial grasses and conservation tillage in the traditional cotton peanut rotation in the southeastern United States (Katsvairo et al., 2006b; Katsvairo et al., 2007). The resultant grass based rotation (or sod based r otation SBR) is consequently more diverse and integrates crop production with livestock farming. The main advantages of this system are: 1) reduced irrigation and pesticide use; 2) favorable environmental impacts like reduced N leaching and increase on soi l organic matter; and 3) less risk and greater profitab ility (Wright et al. , 2012) . These factors are likely to result when SBR is compared to the conventional rotations in which only a couple of crops are intercalated within short periods, leaving the so il in fallow or with cover crops during very few months. In particular, the SBR consists of bahiagrass ( Paspalum no tatum Fluegge) grown for two years prior to row crops (i.e. cotton Gossypium hirsutum L. and peanut Arachis hypogaea L.) in a conservation t illage system as follows: bahiagrass bahiagrass cotton peanut (Wright et al., 2012). Previous studies have shown increases in peanut and in cotton yields with the SBR (Marois and Wright, 2003; Katsvairo et al., 2006b; Dickson and Hewlett, 1989; Elkins et a l., 1977; George et al. , 2013). Higher yields of peanuts grown in a bahiagrass rotation are reporte d o n the order of 1500 2500 kg ha 1 , or increases of 15 30 percent with respect n bahiagrass rotations are still not well understood , but are believed to be related to reduction of nematode s and plant diseases as well as increased rooting depth (Katsvairo et al., 2007). Within the SBR, strip tillage (a conservation tillage practice) r esulted in higher
24 peanut yields than conventional tillage (Marois and Wright, 2003). It is possible that the higher yields were due to positive impacts on physical properties of the soil (Katsvairo et al., 2007). In spite of these reported impacts on the environment and crop yields, SBR is still not widely adopted. However, there are no studies assessing the factors that may inhibit or favor the implementation of this system in the southeastern United States. This project assessed the adoption potential of SBR in farms of the Southeastern United States, and the conditions / factors that encourage or discourage adoption . In addition, knowledge about the perception of farmers regarding the technology cons and pros are also explored. These perceptions are impo rtant for providing insights to further adoption strategies design and are also important to determine if the awareness of the farmer about SBR affect his willingness to adopt this practice. Factors that influence the adoption of conservation practices in agriculture The factors that influence SBR adoption may be diverse and specific to the site conditions. Although there are no studies that assess the adoption potential of SBR specifically, other adoption studies conducted for conservation agriculture arou nd the world can provide insights into relevant factors influencing decisions of farmers to adopt these practices. It is worth mentioning that the package of practices involved in the proposed SBR can be defined as conservation agriculture. The FAO (2001) has aggregated into conservation agriculture the practices that aim to improve the use of agricultural resources (relative to conventional agriculture) through the integrated management of available soil, water and biological resources such that external i nputs (e.g. fertilizer, herbicides, etc.) can be minimized (FAO, 2001; Garcia Torres et al., 2003). These practices include conservation tillage, be it minimum or no till, the use of cover crops, extensive crop rotations, and straw mulch (Knowler and Brads haw, 2007).
25 Previous studies show that conservation practices in agriculture are an attractive option for farmers because these practices have the potential to reduce production costs (e.g. Allmaras and Dowdy, 1985; Quintero, 2009) and generally produce hi gher net returns in developed and developing countries (e.g. Stonehouse, 1997; Sorrenson, 1997; Knowler, 2003; and Sorrenson et al., 1998). However, there is still a gap between the maximum potential adoption, in terms of existing cropland area, and the cu rrent adoption levels. On a worldwide basis, about 80 million ha, representing 5% of the total arable land worldwide, is currently farmed with conservation agriculture (Garcia Torres et al., 2003). In the USA, 41% of the cropland employs conservation agric ulture (CTIC, 2004) and the adoption of SBR seems to be incipient or at the experimental level. This situation may indicate two things: i) technological research and promotional work has been useful to some extent but these alone may have had a limited imp act on the adoption of technologies (Lapar & Pandey, 1999); ii) there is a need to identify those factors beyond just farm finances (especially, beyond net profit) that explain adoption and non adoption (Knowler and Bradshaw, 2007). Previous studies have i ndicated that incentives for adopting conservation technologies depend on farm and farmer specific characteristics, technology specific conditions (Ryan and Gross, 1943; Lapar and Pandey, 1999) and external aids (e.g. information, subsidies, etc.) (Knowler and Bradshaw, 2007; Conley and Udry, 2001). According to previous studies, farm and farmer specific characteristics such as climate variability, slope, soil quality, crops grown, farmer age, level of education, off farm employment, tenancy, gender, availa bility of cash and labor, access to capital market
26 and Zilberman, 2001; Korsching et al., 1983; Lapar & Pandey, 1999; Reardon and Vosti, 1997). Similarly, technology spe cific characteristics can affect adoption behavior. Although this is recognized, technology characteristics based approaches to study consider the farmer preferences for tho se characteristics or how these preferences vary across farmers (Useche et al., 2009). In the context of this study, the technology specific characteristics refer to those specific features of the proposed technology that have to be followed by farmers in order to make the technological change (e.g. new tillage methods, additional labor consuming activities, livestock integration into crop rotations, reduction of fertilizers use, etc.) or features derived from the adoption of the technology (e.g. net return s, reduction of crop pests and diseases incidence, etc.). Regarding external aids for the adoption of technologies, availability of information about the technology and how it flows to farmers may influence adoption. Farmers may learn from extension agent s or from neighbors, and this can lead to different adoption results (Conley and Udry, 2001). Thus, this wide range of variables that can affect adoption shows that the expected profit from a new technology is only one dimension among many to be considered when analyzing adoption. Furthermore, farmers may be reluctant to adopt new practices if they are risk averse due to uncertainty in the return and productivity benefits of conservation practices , if sunk investments are needed (Kurkalova e t al., 2006), if the required skills and technical assistance is lacking (Kern & Johnson, 1993), or if specific operation constraints exist.
27 Previous studies have reported mixed conclusions about the effect of farm; farmer and technology characteristics; and external aid For example, farmer age has a negative effect on adoption of soil conservation practices in both Nebraska (Hoover and Wiitala, 1980) and the Philippines (Lapar and y affected the adoption of conservation tillage (Kurkalova et al., 2006). Similarly, land ownership also has a mixed effect on adoption of conservation tillage (Soule et al., 2000). In general, it is expected that farmers with secure land tenure will have incentives to conserve soil for future benefits. However, some farmers behave as owners (e.g. kinship relations) and this behavior translates into a positive effect in the adoption of conservation practices (Lapar and Pandey , 1999 ). Also, higher adoption of conservation tillage was found in farmers working offsite (e.g. Fuglie, 1999; Korsching et al., 1983). In addition, Knowler and Bradshaw (2007) reported studies where off farm income has had negative or insignificant impact on adoption of these practic es. Similarly, the same authors showed no conclusive results for education level, farm size, farm income, conservation agriculture subsidies programs, and land slope; insignificant, positive or negative effects of these variables on the adoption of conserv ation practices were found. These results indicate that common wisdom assumptions are not always confirmed (e.g. land ownership has not always been correlated with higher adoption levels of conservation practices, nor has higher proportion of off site inc ome in the households). The effect of other variables seems more consistent across various studies. In general, access to markets and well developed land markets (Lapar and Pandey , 1999 )
28 and access to information about the conservation technology ( e.g. Tr aore et al., 1998; de Herrera and Sain, 1999, Conley and Udry, 2001) have been found to positively affect the adoption of conservation practices. Environmental variability is hypothesized by Kurkalova et al. (2006) as having a negative effect on adoption of conservation tillage. On the other hand, women are in general more risk averse than men (e.g., Barsky et al. , 1997; Jianakoplos and Bernasek 1998). The non conclusive results about the effect of several farm and farmer specific characteristics on the adoption of conservation agriculture technologies may be caused by differences in site specific conditions. For example the variability of slopes between given soil co nservation technology. Or the low fertilizer use advantage of a technology may not be a feature that significantly explains the adoption decision on a site were farmers benefit from fertilizer subsidies and inversely, can be a significant feature explainin g adoption decisions in a site with non subsidized fertilizers. Similarly, the effect of access to credit markets may have a null effect in wealthy areas and a significant effect in poorer ones with not well developed capital market for smallholders. Knowl er and Bradshaw (2006) conducted a meta analysis about the adoption of conservation agriculture. The main finding was that few if any universal variables regularly explain the adoption of conservation agriculture across existing studies. In this sense, ado ption studies should consider as much as possible a wide range of farm, farmer and technology characteristics as well as the external aids farmers received, in accordance with the particular local conditions. This broad approach provides insights about the extension strategies and the proper incentives. For example, an extension
29 strategy that tries to overcome a perceived net loss in profit would differentiate from a strategy oriented to farmers with risk aversion or information weaknesses. Factors that ma y influence the adoption of SBR by farmers in Southeast US According to literature review with respect to the type of effect that different farm, farmers and technology characteristics may have on adoption of conservation practices in agriculture, variable s can be identified and selected as well as its hypothesized effect considering the specific co nditions of the study site. In T able 1, the variables that are likely to explain the adoption potential of SBR in Southeast US are listed. The different variable s selected to explain the adoption of conservation practices in the study site can contribute simultaneously and in a different degree to the adoption decision. The specific hypotheses with respect to the effect of these variables are explained below and s ynthesized in Table 2 1. Land owners are more likely to adopt conservation practices than land holders or land tenants. The rationale is that land owners have incentive to conserve the soil fertility to derive future benefits, while other types of land users (especially renters) are interested in short term benefits from land, resulting in rapid soil fertility exhaustion. This variable is highly relevant as livestock farmers that may be interested in adopting SBR (and incorporate crop production in thei r system) often rent land for grazing or for haying. Younger farmers with high education levels and lower amounts of sales per year (as a proxy of farm size) are more likely to adopt the conservation practices technologies. This is based on the assumption that younger farmers are less risk averse, mixed crop livestock systems may be more suitable and feasible for small farmers than large ones -who have already an important capital investment in commercial mono cropping, and the higher the level of educatio n the higher the awareness and understanding of the environmental and long term benefits of these practices. Farmers that have received training and technical assistance in SBR from extension agents may be more likely to adopt these practices. The amount of available family labor affects positively the decision to adopt conservation practices. Since SBR requires more labor compared to
30 conventional systems, higher availability of family labor will contribute to increase the adoption of these conservation pr actices. Nonfarm income will also have a positive effect on adoption. This is expected as non farm income may improve household liquidity, which is one of the main constraints to adoption (Lapar and Panday, 1999), especially when the conservation agricultu re alternatives require new investment (e.g. seeds for cover crops and perennial grasses; hired labor, etc.). In the same sense, farmers that have debt to asset ratio above 40% would be more averse to adopt new practices as they would be more risk averse to technological changes due to their already high proportion of debt. Information about the SBR via extension agents, media, neighbors, internet, etc., always results in a positive effect on adoption. However, the magnitude of the effect is different acro ss information flow mechanisms. Regarding subsidies, it is assumed that most farmers in the US can access government payments, so the actual reception of this does not explain differences between potential and non potential adopters. However, access to su bsidies specifically targeted to conservation practices could result in higher adoption levels. Since most of the farm subsidies in the USA are provided to specific commodities (e.g. wheat, corn, rice, peanuts and cotton); bahiagrass lands could be eligibl e for receiving government payments (e.g. via conservation program, and milk subsidies) (EWG, 2014). Therefore, it is hypothesized that farmers that are receiving government conservation payments are more likely to adopt SBR. Higher benefits than incurred costs when implementing SBR may also incentivize the adoption of these practices. The technology cost, fertilizers, herbicides and labor requirements compared to the conventional alternative are also technology features that can influence adoption decisio ns (Useche et al. , 2009). It is hypothesized in general that higher costs, and fertilizers, herbicides and labor use will affect negatively the adoption decision. Farmers already using conservation tillage practices such as strip tillage, using disk alone or practicing no till, as a proxy variable of the awareness of farmers about conservation practices, are more likely to adopt SBR. Therefore, farmers practicing intensive tillage methods are less interested in adopting SBR. Also, crop farmers raising cat tle will be more likely to adopt SBR since they will not have to purchase cattle. Otherwise, farmers without cattle would have to profit from their perennial grasses by renting land for contracting grazing or producing hay for sale. The investment payoff p eriod will affect the farmer adoption decision. As mentioned before, if a long term payoff period is perceived for the conservation
31 agriculture investment, it is likely that this will affect negatively the adoption decision. The payoff can be seen from the farmer perspective in different ways. This includes the payoff in terms of economic returns or only in terms of crop productivity. For our study site and technologies, it is hypothesized that the positive short term effect of the conservation agriculture technologies proposed in North Florida on the crop productivity (i.e. peanuts and cotton, respectively) will increase the likelihood of adoption. Other variables such as access to roads as a proxy of access to markets and land slope were not considered in the study since these do not vary greatly across farmers within the study site. Basically, all farmers of the study site sell their products and there are no differences across farmers in terms of infrastructure for accessing the markets (e.g. road access or proximity to markets). Also, there are no great variations in slope between farms. Methods A structured survey was designed and delivered to farmers located in North Central and North West Florida, South Georgia and Alabama. The sample population for t his study was farmers listed by extension county offices as those that have participated in their extension activities including those related to SBR. This study concentrated in this sample population as the purpose of this study was to assess the willingn to respond the questionnaire. The questionnaire included aspects corresponding to four types of variables that can explain adoption willingness: i) farm characteristics; ii) farmer characteristics, iii) external aids to promote the technology extension, and iv ) technology characteristics. The questionnaire was validated and adjusted based on a pret est made with a set of farmers attending a Field Day at the UF NFREC in 2011, with revisions from social researchers and extension faculty working on SBR.
32 The pretested structured questionnaire was used to collect data on the following variables: Farmer c haracteristics: Gender, education level, internet access and use, off site income, debt to asset ratio, land ownership Farm characteristics: location, crop types (if any), cattle raising, farm size, type of tillage methods, sales per year, availability of family and hired labor External aids: SBR information flow mechanism to farmers, reception and type of government payments Technology characteristics costs of the technology, benefits of the technology in terms of crop productivity, pests and diseases reduction, environmental stewardship, required inputs and time requirements. Adoption potential: The farmer was asked if he/she plans to practice SBR within the next 3 years (dependent variable). In addition, farmers were asked the reasons for planning or not practicing SBR within the next years. Also, farmers that were not willing to practice SBR in the upcoming years were asked if they would be willing to plant bahiagrass in unproductive land (if they have s ome in their farms) as an initial step to improve those areas with the perennial grass. The survey was mailed in winter 2012. Questionnaire design and administration followed best survey practices (Dillman, Smyth, and Christian 2009). The surveys were mai led to a randomly selected sample of 393 farmers in North Florida (Central and West), Georgia and Alabama. As mentioned, this sample was obtained from databases listing farmers that have been involved in SBR extension activities that were provided by exten sion faculty of the different counties. However, the questionnaire also included a double check question asking if this was the first time the farmer heard about SBR. In the case of a positive answer, the survey was not considered when analyzing the variab les that may explain the farmer willingness to use SBR or not in the upcoming years.
33 An announcement letter explaining the purpose of the survey was mailed on December 4, 2012. The first survey package was mailed on December 11, 2012 which included a cov er letter, questionnaire, a pre paid return envelope and a souvenir. On December 17, 2012, a postcard was mailed to thank respondents for participation and remind non respondents to fill out the survey. All these materials are available in Appendix A. To relate the different variables (independent variables) with the willingness of farmers to adopt or not adopt the technology within the next 3 years (the dependent variable), a probit model was estimated using STATA (v.13) by a maximum likelihood method. In the model, the dependent variable takes the value of 1 if the farmer plans to practice a SBR within the next three years, and 0 otherwise. Results Of the 393 surveys mailed, a total of 119 surveys were returned of which seven respondents refused to resp ond. Ninety had sufficient data to enter in the analysis and were those respondents that stated that this survey was not the first time they heard about SBR. The effectiv e response rate was 30% (Table 2 2 and 2 3) . Sample descriptive characteristics Table 2 5 shows the percentage of farmers that are willing to practice SBR within the next three years. I t was observed that 54.4% of surveyed farmers with previous potential adopter region with higher percentage of farmers that are planning to practice SBR while North West Florida has the higher percentage of farmers that do not plan to implement SBR (data not sho wn).
34 Descriptive statistics on the farm and farmer characteristics of potential and non potential a dopters of SBR are reported in T able 2 4. In summary, it is perhaps worth noting that most potential adopters raise cattle (71%) and grow crops (98%), especi ally peanuts and cotton (85%), feed animals with other sources different to hay and grazed pastures, use intensive tillage methods, and have more availability of family labor compared to non potential adopters. Regarding farmer characteristics, 56% of pote ntial adopters are older than 50 years old (78% of non adopters are in this age range), and have less time farming (27 vs. 34 years in non potential adopters). Surprisingly, there are not major differences between potential and non potential adopters in t erms of off farm work, government payments, in the debt to asset ratios, gender type, farm sales amounts, and education level. In general 30% of the full sample have off farm work; 72% of them receive government payments out of which only 8% receive conser vation payments; only 8% have debt to asset ratios greater than 40%; the average education level is high school degree and only 7% have college degree or more; and 24% of the sample have annual farm sales in the lowest range (i.e. $50,000 99,000). Reason s for planning to use or not sod based rotations Important reasons for farmers to be willing to practice SBR within the next three years were mainly increase in crop yield (47.5%); reduction in pest and diseases incidence (22.5%); and higher profitability (17.5%). Reduction in input costs, reduction of risk and improvement of environmental stewardship were reasons expressed by a lesser amount of farmers (Table 2 5). On the other hand, the major reasons that keep farmers from practicing sod based rotation w ere current farm activities are performing well (36.7%), farmers are not
35 interested in integrating row crops in the livestock operations (22.4%), or farmers are not interested in incorporating cattle into row crops operations (16.3%). Some few farmers also responded that they lacked required equipment to practice SBR (4.1%), the price of peanuts or corn do not incentivize the adoption of SBR (4.1%), there are financial constraints or they believe that row cropping is more profitable (6.1%) (Table 2 6). In addition, 55% of non potential adopters responded that overall the benefits of the sod based rotation are slightly or significantly greater than the costs, 21% responded that costs and benefits are about equal, and 24% that costs are slightly or significan tly greater than benefits. Also, 63% of non potential adopters have some parts of their farm fields that do not yield as high as they want or expect. However, only 22% of them would be willing to consider putting these portions of their farms into bahiagr ass or another perennial grass for 2 3 years to see if the quality of that land improves. It is worth mentioning that farmers for whom this survey was the first time they heard about SBR (18% of the sample), 42% responded to be willing to put perennial gra sses in parts of their farms that are not yielding as they expect. Factors influencing adoption of SBR The decision to adopt SBR is theoretically a function of four sets of factors: (1) socioeconomic characteristics of farmers, (2) farm characteristics, ( 3) technology characteristics and (4) external aids. In this sense, different empirical models were tested including variables from the four sets of factors. The results of the empi rical model are given in Table 2 7. The model gave 75.9% correct prediction s of potential adopters and non potential adopters. Seven explanatory variables were significant in explaining potential adoption decisions of farmers. Results show that the probability of
36 potential adoption is higher for (a) farmers that feed animals with other sources different from hay and grazed pastures (e.g. silage, bulk feed, pellets, gluten, balage, soyhulls, seed, ground corn); (b) farmers that first heard about SBR through the visit of an extension agent to his farm; (c) farmers that do not receiv e government payments; (d) farmers that uses intensive tillage methods (e.g. disk plus moldboard plow, disk plus paratill plus strip till, disk alone, disk and bedder, rip till, rip and bed, fall paratill, spring striptill deep banded fertilizer, disk and ripper, tiller and bottom plow) 1 ; ( e) farmers growing peanuts and cotton; (f) farmers with farm sales per year in the range of 50,000 99,999 and (g) farmers under 50 years old. It is worth mentioning that 34% of potential adopters seem to have already m ixed crop livestock systems, as they responded positively to the question about having pastures where they have previously grown crops, and currently do both, grow crops and raise cattle. Technology dissemination . mechanisms through which they first learned about SBR was demonstration by responded that the primary source of information of SBR keeps being the cooperative e xtension service (55 %) and other farmers (26%). Regarding, events survey responses showed that 36% of farmers have participated in very few (1 2 events) or in any (36%). Regarding their overall perception about knowledge and skills required in SBR technology, although 72% of them believe that benefits of implementing SBR are 1 It was considered as non intensive tillage methods, no till, strip tillage and disk plus chisel.
37 for training/education in sod most of them perceive that they h ave the necessary technical/mechanical and agronomical skills for imple menting SBR effectively (Table 2 8). Discussion The empirical model obtained in our analysis shows the farm and farmer characteristics which contributed significantly to the probabilit y of occurrence of a positive adoption decision. This does not denote a causal relationship between the willingness to adopt and the characteristics of farm and farmers; instead, this indicates what characteristics are for non potential adopters and for po tential adopters. Also, since this study investigated the willingness of farmers to adopt SBR and the characteristics that correlate to this willingness, endogeneity is not expected to be a main issue among the variables in the model. In other words, the v alue of an independent variable (i.e. farm and farmer characteristics) cannot be attributed to SBR adoption per se , as adoption has not occurred yet. The significant variables in the empirical model were related to farmer, farm and extension service vari ables. These allowed us to characterize the type of farming system managed by potential adopters. Basically, these significant variables indicate that these farming systems correspond mostly to relatively low income farming operations (US$50,000 99,999 p er year) managed by farmers 50 years old or younger that have been visited by extension when they first heard about SBR. The system is based on farms with conventional peanut cotton rotation which have cattle partially or totally fed with supplements and u se intensive tillage methods. Although SBR can be practiced in any crop rotation it is worth noting that the described system is the one used in most SBR related research and extension work done by UF NFREC.
38 The most important characteristic associated wi th potential adopters is the use of sources of cattle feed different or in addition to hay and grazed pastures. Although the reasons for this correlation were not explored further in the survey, this could be explained by the high costs of cattle supplemen ts. Also, this confirms that SBR adoption occurrence is more likely among farmers that already have cattle, which is in line with the econ omic analysis results shown in C hapter 3. Farmer age and farm size have the expected effect in the potential adoptio n model. Basically the results are in line with the study hypothesis that younger farmers operating low income farms are more willing to use SBR. Previous studies have attempted to explain similar results by hypothesizing that older farmers have shorter ti me horizons and are less inclined to invest in novelties (Diederen, et al. , 2003) or by arguing that younger farmers are more in touch with external sources of information and, consequently, with innovations available in the market (Schnitkey et al.,1992) . Regarding farm size, the negative relationship found in this study could be explained using the same arguments that Olmstead and Rhode (1993) and Hategekimana and Trant (2002) provided. These authors explained that small farmers are more willing to be ea rly adopters since they are looking for new opportunities for improvement and are willing to take risks. However, as explained in the overview section , farm size is a variable that depends on the site specific characteristics and its behavior with adoption decision may change from one site to the other. In fact, according to Diederen et al. (2003), most previous adoption studies have found a positive relationship between size and adoption.
39 On the other hand, the proposed hypotheses for the variables related to the current use of intensive tillage methods and the reception of government payments were rejected. Farmers currently using intensive tillage methods were expected to be less interested in adopting SBR as they might be less aware of the deleterious im pact of those practices on the environment and the productive capacity of their soils. However, the results showed a direct relationship between willingness to adopt SBR and current use of intensive tillage methods. The reasons for this relationship were n ot further explored by this study. However, these results may indicate that farmers using conventional tillage practices may already be aware of the negative effects of these practices and consequently, could be interested in implementing soil conservation practices. With respect to subsidies, the reception of conservation payments did not explain the willingness of farmers to adopt SBR, as expected. This can be explained by the very small number of farmers receiving such payments. However, unexpectedly, f armers that do not receive any type of government payment are more likely to adopt SBR in the near future. Non effect was initially expected from this variable since it was assumed that most farmers in US can access these subsidies. This result could be ex plained by the fact that Florida receives a relative small proportion of the national subsidies. In 2009, Florida obtained only 0.7 percent and 5.4 percent of the national subsidies provided to cotton and peanut growers, respectively. Also, only 9.8% of Fl orida farms receive government payments (EWG, 2014). As an example, one of the farms in North Florida with the highest collection of government payments from 1995 2009, received over $230,000 from which 94% were disaster related payments and the remaining 6%
40 corresponded to commodity related subsidies. Thirty three and 9 percent of commodity subsidies were cotton and livestock subsidies, respectively. The remaining part was corn, wheat, sorghum and oats subsidies (EWG, 2014). Perceptions of farmers about S BR Although, many adoption studies tend to focus only on describing patterns of adoption based on farm or farmer characteristics, the perception that farmers have of technology characteristics and the context of farmers is crucial to understand why farmers make certain technology decisions. This is crucial for adjusting the technology and extension methods, and identifying contextual bottlenecks that can be overcome by policy decisions, etc. Otherwise, focusing only on identifying characteristics that disti nguish potential adopters from non potential adopters may imply a tacit assumption 1993). Although none of the technology related variables were significant in the adoption e those SBR aspects that are important when considering adopting SBR. These reasons are crucial when designing SBR extension strategy. The most important motivations explaining t he willingness of farmers to practice SBR were the expected increase in crop yields and the reduction of pests and diseases. Several studies have shown that farmers are motivated to adopt new farming practices when crop yield increases; even though the new practice does not optimize the net returns of their firms . According to Reardon and Vosti (1997), if the payoffs of conservation investments in farms appear t o occur in the long term, these type s of investment s will be of low priority for risk adverse an d poor farmers. The payoff, however, may be
41 perceived by farmers in different forms than the economic payoff. For example, many farmers based their adoption decision on how soon effects on crop productivity can occur, so if a conservation investment (e.g. SBR) has a short term impact on crop productivity, the adoption of the technology may increase. Also, yield losses associated with the incidence of pests and diseases are very frequent in cotton peanut rotations in Southeastern US, so farmers would recogn ize any reduction in pest related losses . acceptability of these practices. Based on Swinkels and Franzel (1997), feasibility must accompany the ability of farmers to manage the t echnology, and the required information and time to implement such rotation. Perceptions of profitability will generated benefits. Evidence on acceptability can be foun regarding the advantages of SBR and how great these are compared to the disadvantages. Also, it includes risk perception and compatibility with the different types of farming operations in the study site. Based on this, survey resp onses indicated that most of farmers consider that they have the required mechanical, technical and agronomical skills to implement SBR. However, most farmers also believe that more information on SBR may be required in the region. Also, 40% of the farmers agreed that time requirements of SBR are high. Therefore, for some farmers it may not be feasible to incur more labor demanding activities. So for farmers, SBR is feasible considering their current mechanical and agronomical skills, but some may require m ore information on this type of system and
42 for some others labor is a limiting factor. Furthermore, although most farmers disagreed based rotations are aware of the SBR benefits, still 37% of non adopter were neutral or agreed with this statement. From the profitability perspective, even though 76% of farmers do not believe that SBR costs are greater than its benefits, only 46% are willing to practice S BR in the upcoming years, indicating that other factors may be interacting when considering adoption. This indicates that profitability aspects are not the main reason for having 54% of the surveyed farmers not planning to use SBR practices. Instead, it se ems that most reasons regard acceptability. The main reason for non adoption in the upcoming years is that their current overall systems are performing well or they are simply not interesting in mixing crops and livestock operations. Previous studies have shown that farmers often reject a technology that initially appears to be reasonable because the proposed technological change was basically not consistent with the rest of the farming system. In other words, the non adoption was not due to a specific qual ity of the technology (CIMMYT, 1993). Similarly, in the case of the present study, the results may be indicating two things: 1) SBR is incompatible with their current operations, which are performing well and receive government payments, and 2) SBR may not meet the opportunity costs of changing from conventional systems to a pasture based crop rotation. In addition, if the non reception of any government payment contributes to increase the adoption potential likelihood as suggested by the results, then the subsidies might be actual incentives to continue operating conventional non mixed farming systems.
43 On the other hand, SBR may imply an opportunity cost equal to the difference between the highest returning practice and the conservation practice (Antle et a l., 2007). It is generally assumed that a farmer will be willing to change if that opportunity cost is compensated for or if the new alternative produces equal or higher net returns. However, economic analyses show that for conventional farmers, the implem entation of SBR has an opportunity cost that would need to be compensated for somehow to incentivize its adoption. However, as mentioned above, for those farmers that rely on supplemented feed for their cattle and that may already perceive negative effects of conventional tillage, the SBR may represent a likely option that provides more feed alternatives for cattle and reduces deleterious impacts of intensive tillage. These benefits may have a value for farmers resulting in non opportunity cost. Technology dissemination recommendations and opportunities Results from a formal survey, in the context of an adoption study, are expected to provide insights that, combined with other extension activities, will help to refine technology alternatives for farmers (CIM MYT, 1993). In this sense, the results in this study provide recommendations allowing us to envision opportunities for technology transfer. Extension visits to farms are worth maintaining and promoting as these are shown to be a mechanism through which fa rmers that are willing to adopt SBR, first learned about SBR. Although this does not indicate a causal relationship between willingness to adopt and the extension mechanisms, it still reflects that it is the means by which most farmers learn about the tec hnology. In contrast, farmers are less likely to learn about SBR through the media. Also, many farmers do not use the internet on a daily basis nor do they attend many SBR events. Then, cooperative extension service
44 with farm visits is a worthy extension s ystem to maintain and promote when trying to communicate and disseminate knowledge on new management practices, technologies, etc. On the other hand, even though one of the main reasons for not planning to use SBR is that current systems are performing wel l, there is still a percentage (22%) of non potential adopters that are willing to establish perennial grasses in parts of their land that do not yield as they want. Also, 42% of farmers with any knowledge of SBR will be more willing to do so. This provide s insights about opportunities to have the technology tested in its early stage by some farmers, who may eventually find it advantageous to migrate into a mixed crop livestock system. Extension messages regarding the technology should stress the scientific evidence regarding the positive effects of SBR on crop yield and on the reduction of pests and diseases incidence, as these are the main motivations of farmers willing to adopt SBR, as above mentioned. To summarize, since this study is the first adoption analysis relating to SBR, the results present an opportunity to design further surveys and assessment to follow potential adopters over time and observe likely patterns in the process of SBR adoption. Data collected over time regarding the status of adopti on may be useful to understand how innovative the farmers are or at what stage of adoption they are (i. e. early adopters, late adopters or non adopters). Summary As the first adoption analysis relating to sod based rotations (SBR) in Southeastern US, this study has demonstrated that 46% of Southeastern US respondents are willing to practice SBR within the next three years. The most important
45 yields and; the reduction o f pests and diseases. On the other hand, reluctance to use SBR was explained as being due to: the high performance of current systems; or The empirical model develo ped in this study demonstrated that the probability of potential adoption is higher for: (a) farmers that feed animals with products other than hay and grazed pastures; (b) farmers that first heard about SBR through the visit of an extension agent to their respective farms; (c) farmers that do not receive government payments; (d) farmers that uses intensive tillage methods; ( e) farmers growing peanuts and cotton; (f) farmers with farm sales per year in the range of USD50,000 99,999; and (g) farmers under 50 years old. These farm and farmer characteristics are typical of the production system used in most SBR related research and extension work carried out by UF NFREC. Extension visits to farms have largely been the mechanism through which farmers who are willing to adopt SBR, first learned about SBR; as such, visits are worth maintaining and further promoting. Extension messages should stress the reduced incidence of pest main motivations for SBR adoption. Although, 54% of farmers are not willing to practice SBR within the next three years, there is still a significant percentage (22%) of non potential adopters who are willing to establish perennial grasses in parts of their land that provide insufficient yields. This willingness may indicate an opportunity to convince such farmers to test the technology in its early stage. In such events, farmers who find the approa ch advantageous might subsequently migrate to a mixed crop livestock system. Finally, results from this study serve as a baseline upon which future data,
46 collected over time and regarding the status of adoption, might provide insights about: adoption rates stage status (i. e. early adopters, late adopters or non adopters).
47 Table 2 1. Selected independent variables to explain SBR adoption Variable Hypothetical effect on willingness to adopt Farmer cha racteristics Age Education level + Male gender Nonfarm income Debt to asset ratio Owner of land Sales per year + + + Farm characteristics Have livestock and crops + Availability of family labor + Usage of intensive tillag e methods Technology characteristics Expected marginal returns + Effect on crop productivity + Technology costs Labor requirements Fertilizers and herbicides use Pests and diseases incidence External aids Information abou t the technology + From training and extension services + From neighbors (social networks) + Subsidies from conservation programs + Note: Positive effect (+) indicates a direct relationship between the variable and the adoption decision. Likew ise, ( ) denotes an indirect relationship. Table 2 2 . Survey responses Item Number # Surveys sent # Surveys returned # Returned but refused to respond # Surveys returned and answered # Surveys retu r ned incomplete or Effective response ra te 393 119 7 112 22 30% SBR
48 Table 2 3. Descriptive statistics about the adoption potential of SBR Do you plan to practice a sod based rotation within the next three years? Freq. Percent No 49 54.44 Yes 41 45.56 Total 90 100 .00
49 Table 2 4. Descriptive statistics about potential and non potential adopters of SBR Variables SBR adoption potential Mean ( Full sample) Non potential adopter Potential adopter p val ue Farmer characteristics Farmer age (50 yrs or older) 0.78 0.56 0.03 * 0.68 Female farmers 0.04 0.00 0.16 0.02 Education level 3.02 2.80 0.33 2.92 Farmers with college degree or more 0.08 0.05 0.53 0.07 Time farming (# of yrs.) 34.46 27.17 0.02 * 31.15 Uses internet on a daily basis 0.41 0.39 0.86 0.40 Debt to asset >= 40% 0.08 0.07 0.88 0.08 Work off farm 0.28 0.32 0.63 0.30 Acres owned (ac) 738.77 672.90 0.85 708.48 Number of acres rented 452.34 652.90 0.46 544.55 Income in the $50,000 99 ,999 range 0.24 0.24 0.99 0.24 Farm characteristics Farm located in Alabama 0.12 0.13 0.97 0.12 Farm located in North Central Florida 0.29 0.50 0.04* 0.38 Farm located in North West Florida 0.55 0.32 0.03* 0.45 Farm has crops 0.69 0.98 0.0 0* 0.82 Crop area (ac) 748.25 1036.70 0.38 908.50 Farmer grows cotton and peanuts 0.59 0.85 0.00* 0.71 Use intensive tillage methods Â§ 0.49 0.78 0.00* 0.62 Farm with cattle 0.51 0.71 0.06** 0.60 Pastures area (ac) 238.04 284.07 0.68 263.23 Sources of feed for cattle other than grazed pasture or hay Â¶ 0.06 0.24 0.02 * 0.14 Family members working on the farm 1.82 2.39 0.08** 2.08 People hired per year (# of people) 2.83 6.95 0.20 4.68 External aids First learned about SBR by means of a visit by extens ion to farm 0.53 0.56 0.78 0.54 Receive government payments 0.72 0.73 0.99 0.72 Education level: the variable could have an assigned value of 1 6 as follows: 1: less than high school; 2: High school degree; 3: High school degree and some college; 4: College degree; 5: College degree and more; 6: Advanced degree. triticale, peas, sunflowers, cucumbers, watermelons, carrots, green beans, t obacco Â§Intensive tillage methods include: Disk plus moldboard plow, disk plus paratill plus strip till, disk alone, disk and bedder, rip till, rip and bed, fall paratill, spring striptill deep banded fertilizer, disk and ripper, tiller and bottom plo w Â¶ O ther sources include: bulk feed, pellets, gluten, balage, soyhulls, seed, corn or bean silage, ground corn. *Significant at 5%;** Significant at 10%
50 Table 2 5. Primary motivation to practice SBR within the next 3 years % of potential adopters R eduction in input costs 7.5 Reduction in pest and diseases incidence 22.5 Higher profitability 17.5 Increase in crop yields 47.5 Improve environmental stewardship 2.5 Makes farming more interesting 0.0 Reduces risk 2.5 ose only one category Table 2 6. Primary reason that keeps farmers out from practicing SBR % of non potential adopters Current farm activities are performing well 36.7 Lack of additional labor to allocate in new activities 0.0 Lack of required mechanical and agronomic skills 0.0 Lack of required equipment 4.1 Not interested in incorporating cattle into row crops operations 16.3 Not interested in integrating row crops in the livestock operations 22.4 Lost income in the implementation of the practice 0.0 Price of peanuts contracts or high price of corn 4.1 Financial constrains or row cropping is more profitable for his operation 6.1 Not enough sod to tear up 0.0 No longer farming or disabled 10.2 category
51 Table 2 7. Probit model of adoption of sod based rotation in Southeast US Variables Expected effect Coefficients Marginal effects Farmer characteristics Farmer age (50 yrs or older) 0.686 * 0.172 Number of acres rented 0 .000 0.000 Income in the $50,000 99,999 range + 0.921 * 0.231 Education level is college degree or more + 0.162 0.041 Debt to asset >= 40% 0.884 0.222 Farm characteristics Farm located in North Central Florida n.e 0.018 0.005 Farm located in North West Florida n.e. 0.704 0.182 Farmer grows cotton and peanuts Â¶ 1.357 ** 0.341 Use intensive tillage methodsÂ§ 0.933 * 0.234 Farmer raise cattle + 0.346 0.087 Sources of feed for cattle other than graze d pasture or hay n.e. 2.01 3 *** 0.506 External aids First learned about SBR by means of a visit by extension to farm + 0.705 * 0.177 Receive government payments neutral 0.818 * 0.205 Technology characteristic Fa rmer agrees on that SBR reduce risks due to weather, pests and diseases n.e. 0.268 0.067 Constant 1.829 * Obs ervations 83 Log likelihood 37. 32 Chi square statistic for significance of equation 39.83 Degree of freedom for chi square stati stic 0.00 Pseudo R squared 0.34 Cases correctly predicted 75.9 0 *** p<0.01, ** p<0.05, * p<0.1 ground corn. s include: disk plus moldboard plow, disk plus paratill plus strip till, disk alone, disk and bedder, rip till, rip and bed, fall paratill, spring striptill deep banded fertilizer, disk and ripper, tiller and bottom plow Â§Other farmers first learned about SBR by demonstration by extension, from another farmer, extension material or media, family member that already practiced SBR, at High school, reach personally the extension office or just due to its own knowledge Â¶ Other crops farmers grow: soybean, sorgh um, wheat, corn, oats, milo, millet, sweet potato, alfalfa, timber trees, triticale, peas, sunflowers, cucumbers, watermelons, carrots, green beans, tobacco n.e. non effect anticipated or hypothesized
52 Table 2 and benefits Statements Level of agreement Non potential adopters (%) Potential adopters (%) Chi Squared Test There is a great need among growers for training/education in sod based rotation technology Strongly disagree 4.1 0.0 Pearson chi 2(4) = 9.5397 Pr = 0.049 Disagree 2.0 2.4 Neutral 30.6 9.8 Agree 40.8 68.3 Strongly Agree 22.4 19.5 Time requirements for managing a sod based rotation are high Strongly disagree 2.1 5.0 Pearson chi2(4) = 6.2638 Pr = 0.180 Dis agree 16.7 22.5 Neutral 47.9 25.0 Agree 22.9 40.0 Strongly Agree 10.4 7.5 Sod based rotation helps to reduce risks due to weather, pests and diseases Strongly disagree 2.1 2.4 Pearson chi2(4) = 2.7753 Pr = 0.596 Disagree 4.3 2.4 Neu tral 14.9 4.9 Agree 51.1 61.0 Strongly Agree 27.7 29.3 d based rotation is economically viable only for large farms Strongly disagree 8.3 22.0 Pearson chi2(4) = 5.2278 Pr =0.265 Disagree 50.0 46.3 Neutral 31.3 19.5 Agree 8.3 12.2 St rongly Agree 2.1 0.0 I do not have the necessary technical/mechanical skills for implementing sod based rotations effectively Strongly disagree 15.2 17.1 Pearson chi2(4) = 3.3045 Pr = 0.508 Disagree 41.3 56.1 Neutral 28.3 17.1 Agree 13.0 9.8 Strongly Agree 2.2 0.0 Benefits of implementing sod based rotations are not yet proven Strongly disagree 15.2 35.0 Pearson chi2(4) = 10.1838 Pr = 0.037 Disagree 47.8 47.5 Neutral 26.1 15.0 Agree 10.9 0.0 Strongly Agree 0.0 2.5 I do not have the agronomical skill required to use the technology effectively Strongly disagree 10.9 20.0 Pearson chi2(4) = 3.3790 Pr = 0.497 Disagree 52.2 52.5 Neutral 23.9 17.5 Agree 13.0 7.5 Strongly Agree 0.0 2.5 Potent ial adopters; n = 49; non potential adopters, n = 41
53 CHAPTER 3 EX ANTE IMPACT ASSESSMENT OF SOD BASED ROTATION ON PEANUT COTTON FARMS IN THE SOUTHEASTERN US Overview of Research Problem In the latter half of the 20th century, US farms become increasingly specialized and consequently, livestock farming was overwhelmingly separated from crop farming (Gardner, 2002). Nowadays, most producers in US grow few crops and few have both livestock and crops. This structural change has been driven by the green revolu tion as well as the perceived benefits of the economies of scale achieved by each farm cultivating fewer crops (suited to the particular farm) and thereby optimizing their respective growing conditions (de Wit, 1992). This trend is associated with the cons olidation of previously separate croplands into larger farms even though many small farms still persist in rural areas (Mac Donald, et al. , 2013). Although additional and more recent data on these trends is needed, Mac Donald et al. (2013) have nonetheles s noted in their recent study that since 2007 the consolidation rate of croplands into larger farms has begun to slow. According to these authors, the volatility of crop and input prices (i.e. energy) in combination with the debates between producers and g overnment bodies about options to cope with this volatility, have increased the financial risk of the agricultural sector. Additionally, consumer preferenc family farms may also result in the favoring of these smaller farm operations (Low and Vogel, 2011). As a result, mid size farms have declined and the number of large and small farms has grown in the country. The decrease of diversified farming systems in the US has generated some unintended consequences for the environ ment and to the financial stability of agriculture. Cultivating fewer crops within a single farm, renders it more vulnerable to
54 the impacts of pests, diseases and weeds and hence prompts the increased use of biocides and industrial fertilizers, which in tu rn has detrimental effects on the environment (de Wit, 1990). Furthermore, specialized farming systems are more susceptible to concentrated price shocks (Mac Donald et al. , 2013) making them vulnerable to fluctuations in economic conditions. This situation has led to some US based farmers and the agricultural research and extension sector considering a return to diversified farming systems, including any combination of the following techniques: cultivation of various crops in rotation; the integration of ca ttle and crops within one single system; and cultivation practices aimed at soil conservation (de Wit, 1990). Diversified agricultural systems that combine conservation tillage principles are able to sustain crop and livestock in a single integrated rotati on and to provide environmental benefits such as carbon sequestration and conservation of biodiversity (Sanderson et al. , 2013). With these practices, the time soil is left bare is reduced and therefore water pollution and sedimentation loss is decreased. Also, crop rotation interrupts pest cycles, reducing yield losses and the use of chemicals. These type of systems can improve efficiency in the use of labor and capital, reduce the risk of specialized systems to price and pests shocks, and pace the impact of agriculture on environment (de Wit, 1990; Duynisveid et al., 1988, Mac Donald et al. , 2013). Sod based rotations (SBR) in combination with conservation tillage practices are an alternative to diversification for small farmers, especially those who do not have the capacity to take full advantage of the economies of scal e in agriculture, receive less g overnment economic subventions, and in some cases grow crops and raise cattle.
55 Sod based rotation incorporates perennial grasses and conservation tillage in Research and Education Center has conducted research on a SBR that includes bahiagrass ( Paspalum n o tatum Fluegge) and strip tillage and is compared to the conventional cotton peanut rotation (PCCP) in the southeastern United States , which also uses strip tillage (Katsvairo et al., 2006; Katsvairo et al., 2007; Wright, et al., 2012; George et al. , 2013). In the SBR, the bahiagrass ( Paspalum n o tatum Fluegge) is grown for two years prior to the cultivation of row crops (i.e. cotton Gossypium hirsutum L. and peanut Arachis hypogaea L.) in a conservation tillage system as follows: bahiagrass bahiagrass peanut cotton (BBPC) (Wright et al., 2012). Previous studies have shown i ncreases in both peanut and cotton yields as a result of SBR (Katsvairo et al., 2006a; Katsvairo et al., 2006b; Dickson and Hewlett, 1989; Elkins et al., 1977) even when incorporating the grazing in the crop rotation (George et al. , 2013). This lends furth er credence to the argument for SBR, given that cattle grazing have been thought to have a negative effect on soil bulk density and hence crop yield (Miller et al., 1997; Tracy and Zhang, 2008). Furthermore, SBR has helped in: reducing the risk of disease damage (Hagan et al., 2003); improving soil organic carbon; water infiltration; stabilizing soil aggregates (Varvel, 1994; Reeves, 1997; Hagan et al., 2003); and enhancing cotton rooting depth (George et al. , 2013; Katsvairo et al. , 2007). ported benefits in increasing yields, enhancing soil quality and reducing peanut and cotton diseases and pests; this rotation is still not widely used (Siri Prieto et al. , 2009).According to Bowen et al. ( 1996 ) and Hagan et al. ( 2003 ) , the low
56 adoption is explained by the absence of profitable rotation crops. However , no substantial economic studies were found that evaluate the profitability and economic benefits of the whole system over the long term . Such analysis where not only the benefits resulted from higher crop yields and reduced costs on inputs are reflected, but that incorporates also the costs and benefits of introducing a perennial grass and eventually cattle are required to understand all the tradeoffs that this system implies . Such research is important because the selection of a rotation system by farmers depends partly on the net returns for the whole system and not only from individual components of the rotation (Meyer Aurich et al. , 2006 ). Similar economic assessments have been conducted in other systems. The effect of the crop rotation and tillage system on profitability and variation in profit has been assessed in dryland grain farming in the Canadian Prairies by Zentner et al. (2002a, 2002b); and for corn based rotation systems in eastern North America by Meyer Aurich et al. (2006) and Katsvairo and Cox (2000). The profitability of whole rotation systems has been assessed in previous studies using multi year programming models. In a multi year linear programming the impact of activities in net returns is linear, inputs and outputs are divisible and coefficients are known with certainty (Hansen and Krausen, 1989). Morrison et al. (1986) developed linear programming models for the Eastern and Central wheat belt farms in Western Australia, whi ch included rain fed crop livestock farms. Their aim was to determine relative profitability of cropping and livestock enterprises and the most profitable crop pasture rotation. Pearce & Cowie (1986) developed a multi period linear programming farm firm mo del used to study the economics of on farm soil conservation. Hansen &
57 Krausen (1989) created a multi period linear programming model capable of representing farm s in the cereal/grazing regions of South Australia and applied to determine the most profitabl e use of farm resources. Based on the lack of economic analyses assessing the long term profitability of the entire SBR, and the ability of multi year linear programming models to assess the economic performance of complex farming systems, the objective of this study was to determine and compare the economic benefits of the SBR compared to those of the conventional peanut cotton rotation for small scale farmers in the Southeastern US. The economic analysis was done based on a ten year period, focusin g on f our hypothetical farm s that are already in the peanuts cotton business and that each differ as to the type of tillage practices they employ and of their cattle presence and numbers. It is assumed that each f a rm er decision as to whether or not to adopt the SBR depends on the impact of the alternatives on the respective farm profit. Other aspects that can motivate or inhibit the adoption of the SBR were studied in Chapter 2 . Methods Specification of the model In this study, it was assumed that the main motiv ation of any farm is to maximize profits (cumulative net operating surplus) and that the farm is operating under a perfect competition environment where the prices do not vary with the volume of sales (Estrin et al. , 2008). Profit is defined as income for re investment (on farm or otherwise) after all variable costs, fixed costs, tax and private consumption expenditures have been deducted (Hansen & Krause, 1989). In an abbreviate d form, total cost can be defined as the sum of outlays on labor (L); capital (K) and other inputs (I), such as fertilizers, herbicides, etc. (C = wL+rK + vI;
58 where w, r and v are input prices). Since the total cost of production changes with the volume of output and input prices, then the cost production function can be represente d as: C = C(X,w,r,v), where X is the output quantity. Now, the revenue depends upon the quantity of output (X) and its price (p) (R = pX). The costs and revenues of the sod based and the conventional rotation components were entered in a linear programmin g model to maximize the far m profit (objective function) in ten years. Other long term variations to the maximizati on problem such as the buying/selling of land (Hansen & Oborne, 1985) were not included in this study. The model is an adjusted version of th e multi year optimization ECOSAUT model (Quintero et al. , 2006) with a planning horizon long enough to capture the effects of introducing bahiagrass on subsequent crop yields and cash flows. The model was formulated using a linear programming matrix where the objective function (present value of net returns fo r a 10 year planning horizon), farm variables and decision variables were specified. The farm variables were the size of land and the required annual cash flows. These variables are constraints to the system. The decision variables are the likely activities the farm can perform like the possible rotation alternatives (i.e. conventional cotton cotton peanut rotations or the sod based rotation) and the transfer of cash flow for subsequent years. At the end of each year land and cash can be re utilized into the following year. The decision variables and the state variables were related using intermediate information. This intermediate information includes data per crop/pasture of the rotation on the tota l production costs, prices and crop yields in order to estimate the annual net revenues; and the desired allocation of
59 area of each crop depending on the type of the rotation. See Appendix B for more details on the linear programming model. By using the li near programming model, the economic benefits of the SBR were contrasted against those received by a farmer performing conventional peanut cotton rotations. The conventional rotation revenues depended on whether a farmer grows only peanuts and cotton or wh ether the farmer grows these crops and also has cattle but does not integrate both operations in a single rotation. Detailed information on the different rotation alternatives and the costs, prices and yields used in the analyses are described in the follo wing section. Model parameters and crop rotation alternatives assessed The model was run for a farm size of 80 hectares, which is the same size definition used by Katsvairo et al. (2006) to evaluate the SBR in small farms with little equipment and limited labor supply in North Florida. The net present value of net returns over the simulated 10 year period was estimated based on the annual cash flow and applying a discount rate of 5 percent, which has been the discount rate recommended by the Farm Service Ag ency of USDA for at least the past 4 years (e.g. FSA, 2014). The conventional and sod based rotation scenarios (decision alternatives) evaluated in this study and the system characteristics specified in the analysis (constraints) are described below. Const raints: The model was set up so that the maximum available land was 80 ha. When the SBR scenario was run, each SBR sequence was allowed to be carried out in a maximum 20 ha area. The annual cash flow (which is a possible constraint in the model) was left u nlimited, which means that a minimum amount of cash flow was not
60 required at the end of every year. Also, annual surpluses were allowed to be passed onto the subsequent year for their reinvestment in the system if needed. Rotation alternatives and inform ation sources: The conventional peanut cotton cotton peanut (PCCP) rotation practiced with conventional tillage practices a s the baseline scenario in this analysis and against which the sod based rotation (BBPC) was contrasted. The BBPC rotation was creat ed in the model by entering four different sequences (SBR1 SBR4), reflecting that the sequence may vary depending on what is planted in the first year. Each sequence occupies a quarter of the farm and then, every year the farmer has both crop and pasture p roduction (Katsvairo et al., 2006). This SBR arrangement is shown in Table 1. In the design of these rotation sequences the growth of cotton and peanuts after the perennial grass were differentiated from cotton and peanuts grown in a conventional rotation as yields may vary due to the effect of the sod in the rotation (e.g. in Table 3 1 denominated as Peanut_B vs Peanut, respectively). Crop yields, production costs, irrigation needs and amount of chemicals for pest and disease control may vary depending on : i) the type of tillage practices (i.e. conventional or strip tillage practices); ii) the impact of the sod and grazing on crop yields; and iii) the impact on crop yields when reducing irrigation needs in the SBR. The specific rotation alternatives asses sed in this analysis are described below and illustrated in F igure 3 1. The information on yields, costs and prices values used in the assessment of the rotation alternatives are summarized in Table 3 2 3 4 and described for each of the rotation alternat ives. All averaged prices and costs were used within the model as constant prices.
61 Alternatives for a peanut cotton grower: Baseline scenario A1 Conventional peanut cotton rotation: This is the rida performing the typical peanut cotton cotton rotation in fields with irrigation and using conventional tillage. In this rotation, the main sources of revenue are peanuts and cotton lint. Peanut and cotton yield values used in this scenario were the av erage of annual yields reported by the Economic Research Service of USDA for the 2010 2013 period in the Fruitful Rim region in US (ERS, 2014). The production total costs for producing peanuts and cotton with conventional tillage was the average of the to tal costs reported for the past four years by the University of Georgia Cooperative Extension Service (UG CES, 2014). Cotton lint and peanut prices used in the model corresponded with the average of last USDA in the Fruitful Rim region (ERS, 2014). Scenario A2: Conventional cotton peanuts rotation with strip tillage: This rotation alternative is the same sequence as in Scenario A1 but uses strip tillage practices. Consequently, the yields and costs are different. The yield data of cotton and peanuts used in this scenario are the average values reported over the last four years when cultivated with strip tillage and with irrigation supply, obtained from a long term North Florida Research and Education Center (UF NFREC) located in Quincy, Florida (UF NFREC, unpublished data, 2014). In this experiment, sod based rotation and conventional rotation with strip tillage has been investigated since 2010. The yields in this scenario are higher than in the scenario A2 (Table 3 2). The total production costs for producing peanuts and cotton with strip tillage corresponded with the average of the total costs reported for the
62 past four years by the UG CES (2014). Commodity price s are the same as those used in Scenario A1. Scenario A3 Rotation of peanuts and cotton with bahiagrass: In this alternative scenario, SBR is practiced by a peanu t cotton grower in a 80 hectare farm that has irrigation and is divided in four parts of 20 h ectares each, within which particular SBR sequences (as specified in Table 3 1) are being carried out. This scenario not only introduces sod in the peanut cotton rotation but also considers average increases in peanut and cotton yields, as observed in the long term experiment at NFREC at Quincy, FL (Table 3 increases were in the order of 110 to 222 kg ha 1 in cotton and 372 kg ha 1 i n peanuts o n average within the last four years when compared to Scena rio A2. Greater peanut yields in the SBR compared to the conventional rotations have similarly been reported in previous studies (e.g. Dickson and Hewlett, 1989; Hagan et al., 2003; Kastvairo et al., 2007). The production costs and commodities prices are t he same as in scenario A2. The bahiagrass stand in this scenario had different possible uses: for producing and selling hay; or to rent the land under a contract grazing agreement (Kastvairo et al. , 2006; Siri Prieto et al., 2003). These two sub options a re described as follows: Scenario A3 a: The land with bahiagrass rented for grazing (Scenario A3 a). The pasture land rent price used in this scenario is the average price reported by NASS USDA for 2010 2013 in North Florida (NASS, 2014a) (Table 3 4). Th e cost for the establishment and maintenance of the bahiagrass stand used in this scenario was reported by Clemson Cooperative Extension Service (2014). Also, cotton yield was increased in this scenario o n additional 112 kg ha 1 to reach about 224 kg ha 1
63 difference compared to scenario A2. This is based on recent findings at NFREC in Marianna, Jackson Co., Florida, wher e cotton yield increases were about 224 kg ha 1 larger in the SBR system when cotton followed grazed bahiagrass in the rotation, than when it was not (George et al., 2013; D. Wright, personal communication, 2014). These yield increases are attributed to greater residual fertility (George et al., 2013). Scenario A3 b: The bahiagrass is used to produce and sell hay. The price of hay used corres ponded to the average price reported for 2010 2013 by the National Agricultural Statistics Service (NASS, 2014b). The cost of establishing and producing bahiagrass for hay is shown in Table 3 4, and is based on estimates from Alabama Cooperative Extensio n Service (2008). Hay production was simulated at 9.8 ton ha 1 from established bahiagrass. This value was lowered to 7.4 ton ha 1 during the first year of bahiagrass as less biomass production is expected in that year due to the initial time the sod takes to become fully established. Alternatives for a peanut cotton grower that has cattle: Baseline Scenario B1 Conventional peanut cotton rotation and cow calf operation: conventional peanuts and cotton with conventional tillage practices and irrigation, and who also have cow calf operations. In this scenario, the crops and the livestock are not integrated in a single rotation. Half of the farm land is allocated into conventional peanut cotto n rotation and the other half into permanent cow calf operation. Cow calf operations produce and sell calves, which is typical of small farms in the US (Hoppe and Banker, 2010). Yields, prices and costs for cotton and peanuts are the same used in scenari o A1. Production costs and revenues for cow calf operation (Table 3 4) were estimated using the interactive budget developed by the UG CES and adjusted for a
64 stocking rate of 1.5 cow calf pairs per hectare, which is similar to the typical stocking rate rep orted in these systems in the region (e.g. Katsvairo, et al. , 2006). Scenario B2 Conventional peanut cotton rotation with strip tillage and cow calf operation: This farming system follows the same land distribution and crop rotation as in scenario B1. H owever, crops are cultivated with strip tillage and consequently, crop yields and costs are different. Yields and costs of cotton and peanuts are the same as those used in scenario A2. The cow calf operation total costs, stocking rate and revenues are the same as those used in scenario B1. Scenario B3 Rotation of peanuts and cotton with bahiagrass for cow calf operation: In this alternative scenario the same distribution of crops in an 80 hectares farm as in scenario A3 is followed. However, the bahiagra ss is used to graze cattle already owned by the farmer. Crop yields used in this scenario were the same as those used in scenario A3 experiment performed by the UF NFREC in Marianna, Florida; where larger stocking rate capacity than in typical cow calf operation has been realized (i.e. 2.2 cow calf pairs per hectare) (George, et al. , 2013). In this e xperiment, cattle were able to graze on the second year bahiagrass as well with cattle pulled off for 20 25 days after winter grazing and prior to bahiagrass establishment during the first year. Cost and gross revenues per cow were determined using the int eractive budget developed by the UG CES (2014b) and adjusted for a stocking rate of 2.2 cow calf pairs per hectare and an increase in animal weight of about 22.7 kg. at calving as observed in the long term experiment in SBR (details on this experiment are available in George, et al. ( 2013) ) . Also, the cost of
65 harvested feed reported for conventional cow calf operations by UG CES was replaced by the costs of establishment and maintenance of bahiagrass. For scenarios A3 b and B3, an additional analysis was do ne to determine the effect of possible variations in the irrigation and chemical costs. This consisted of assessing the effect (in terms of net revenue) if irrigation supply for growing peanuts on previous bahiagrass stands is removed and chemical costs fo r controlling pests in peanuts and cotton are reduced by 20% (reflecting the positive effect of sod based rotation on pest populations). Kastvairo et al. ( 2006 ) have reported that irrigation needs g depth. Additionally, RodrÃguez KÃ¡bana et al. ( 1988 ) and Johnson et al. ( 1999 ) reported reductions of root knot incidence ( Meloidogyne arenaria limb rot (Rhizoctonia solani Khun) and stem rot ( Scerotiium rolfsii Sacc.) that affect peanuts in comparison to bahiagrass rotation grown peanuts. To capture this likely reduction in irrigation and pest control costs, the total costs reported for peanuts with strip tillage and without irrigation for 2010 2013 by UG CES (2014) were used. These costs were reduced b y lowering the costs of insecticides and disease control by 20%. The resultant value for each year was averaged. In cotton, the total costs of irrigated cotton with strip tillage was used and lowered in the same proportion as for peanuts. In addition to t he effect of irrigation and chemical use reductions on operational costs, crop yields were also adjusted (based on values reported for cotton and peanuts grown in sod based rotations without irrigation supply at UF NFREC in Quincy, Florida). As shown in Ta ble 3 2, peanuts and cotton yield are
66 cotton yields in the SBR after cattle gr azing were increased to reflect those of Scenario B2, so as to simulate the effect of cattle grazing in bahiagrass on subsequent cotton yields. With these variations in costs and yields, the effect of changes in irrigation and chemicals needs on the net revenue was determined and compared to the net revenues Results and Discussion Economic benefits of sod based rotations for small peanut and cotton growers Net revenues , above total costs, over a 10 yr perio d of conventional cotton peanut rotation and of sod based rotation (SBR) alternatives were estimated. The net revenues obtained for conventional cotton peanut rotation with conventional (intensive) and strip (conservation) tillage practice were compared ag ainst SBR alternatives. The conventional cotton peanut rotation with conventional tillage (Scenario A1 and B1) is baseline with strip tillage (scenario A2 and B2) can be unde rstood as an intermediate system, from a conse rvation perspective between intensive tillage crop rotation and the SBR. The SBR is a step forward for conservation tillage, incorporating perennial grasses in row crop rotations. As explained in the methods s ection, the SBR revenues were estimated for two different types of farmers: those farmers that only grow peanuts and cotton; and farmers whose enterprises have both crop s and cow calf operations, which are nonetheless, not integrated in a single rotation. Table 3 5 illustrates the marginal net revenue a crop farmer could obtain if SBR alternatives were implemented compared to the revenues from the conventional rotation with intensive tillage. The results show that SBR can generate important increases in net revenues (in the range of 278,000 to 490,000
67 dollars over ten years) relative to the conventional peanut cotton rotation that uses conventional tillage practices. Overall, conventional rotation system generates much less revenue from cotton production tha n the SBR and generates negative revenues from peanuts. Negative returns for peanut production are in line with the peanut returns per acre planted (when total costs are considered), as reported by the Economic Research Services for the Fruitful Rim region in 2004 2013. Also, the UG CES has reported negative revenues when peanut prices fall below 0.25 USD per pound (or 500 USD per ton, which is the average prices used in this analysis for the last four years) and yields are less than 4491 kg ha 1 . These neg ative returns do not consider government payments, which can have an effect on the consolidation and permanence of peanuts in the region, especially after 2002 when peanut production was included in the broad based commodity program support (Mac Donald et al. , 2013). Comparatively higher net revenues with SBR are explained by potential enhancement in crop yields, when cotton and peanuts are rotate d with bahiagrass. As shown in T able 3 2, the cotton and peanut yields are almost double (in experimental condi tions) when SBR is employed compared to the average yield reported in the region during the same period of time using conventional techniques (2010 2013). Higher crop yields resulting from SBR compensate for the reduction this system entails in regards to land availability for crops, given that half of the land each year is allocated to bahiagrass cultivation (see Table 3 5, for total area allocated to each crop after 10 years). With respect to differences amongst the three SBR alternatives assessed (Scena rio A3 a, A3 b and A3 c), the net revenue can be maximized in a SBR if the bahiagrass is used to produce hay, peanuts can be grown without irrigation, and cost
68 reductions on pest and disease control are achievable (Scenario A3c). Reductions in irrigation needs in peanuts rotated with bahiagrass are attainable. Katsvairo et al. (2007) found no differences in peanut yields grown in a bahiagrass rotation under rain fed (in an environment with an annual average precipitation of 937 mm) and irrigated conditions . Also, yield records from the long term exp eriment at NFREC summarized in T able 3 2, demonstrate the feasibility of having high peanut yields without irrigation in SBR. Furth ermore, as previously mentioned , rotations of crops with perennial grasses are as sociated with reductions in pest populations (root knot nematodes) in peanuts (Katsvairo et al., 2006b) ; therefore, reduction in pest control costs is also made possible. Higher net revenues in this scenario (A3 c) are explained by higher net revenues fro m peanuts. Although peanut yields in this scenario are lower than peanut yields in irrigated conditions, the costs savings offset slight yield reductions, resulting in higher net income from peanuts. Also the production of hay increases income from bahiag rass compared to income when bahiagrass is rented as land for grazing. This is especially the case during the second year of bahiagrass when higher amount of hay can be produced and the maintenance costs of the sod is lower than the establishment costs dur ing the first year. The high net revenues from peanuts compensate for a slight reduction in cotton revenues due to the absence of grazing in the precedent sod (In Scenario A3 a, cotton yields were higher due to the effect of nutrient cycling by the incorpo ration of cattle in the system). When comparing the SBR with the conventional cotton peanut rotation with conservation tillage practices (strip tillage), the net revenues , above total costs, over a
69 10 year period from SBR are much lower. Increases in crop yields attained in peanut cotton rotations with conservation tillage, are much higher than in the same rotation with conventional tillage. Higher yields combined with more area allocated to these crops after 10 years, maximizes profits for farmers who do not own cattle. In contrast, when cotton peanut farmers incorporate bahiagrass into their crop rotation, income from bahiagrass hay (the most profitable income source when bahiagrass is grown) in the long term does not compensate for the loss of profit due to the reduction in total areas allocated to cotton and peanut. However, peanut and cotton yield have been higher for the past three year region wide than any recent three year period. Economic benefits of sod based rotations for small farmers growing cot ton and peanuts and raising cattle The simulated net revenues for small farms performing s eparate cow calf operation and producing cotton and peanuts via conventional tillage practices (scenario B1) were negative. These estimates considered total costs and did not include government payments. This result is caused by negative revenues from peanuts and cow calf operations. As abovementioned, negative returns on peanuts are already reported in the region by the ERS over the past decade. Negative returns in co w calf operations are also aligned with net returns reported by ERS in farms in the Fruitful Rim region with at least 20 cows and that do not retain calves until slaughter. It is worth noting, that th ese negative net revenues for peanuts and cow calf opera tions are based on full costs as reported by the Agricultural Resource Management Survey (ARMS), which include overhead and asset recovery ; nevertheless, the farmer still can cover the direct costs of these operations and may have positive cash flow .
70 W hen this type of farm incorporates conservation tillage practices, farm income can increase significantly due to the potential to increase yields of peanuts and cotton combined with a slight reduction in production costs (T able 3 6). The boost of crop yields with conservation tillage practices allows the farmer to balance negative revenues from a cow calf operation that is implemented independent of the crop farming activities. The positive effect of higher yields in net revenues does not only apply for the pr ice conditions considered in this analysis. For example, peanut yields increases reported by UF NFREC help the farmer break even at a wide range of prices. According to UG CES (2011), yields above 1.8 ton/acre will break even in a price range of $375 575 per ton of peanuts. When conservation tillage practices and sod are fully integrated into the cotton peanut rotation by farmers who own cattle (scenarios B3 and B3a) the net revenue over 10 years is further increased compared to a farm growing these crop s and raising cattle separately (Scenario B2).This increase is due to an additional enhancement of yield when peanuts and cotton are grown in land previously used to grow bahiagrass. Increases in crop yields in this crop pasture rotation have been further explained by the recycling of nutrients in the system, allowing crops to benefit from the greater residual fertility left by cattle grazing (George et al., 2013). Additionally, negative revenues from the cow calf operation are reduced due to increases in w eight at calving and in stocking rates. Although the net revenue above total costs in cow calf operation is negative, the total revenue for the whole system is much higher in a SBR. It is worth mentioning, that net revenues from cow calf operations in a SB R are positive when considering only the variable costs as reported in
71 Katsvairo et al. (2006) and illustrated in a business model developed by the NFREC (available at: http://nfrec.ifas.ufl.edu/sodrotation.htm) . S imilar to the results obtained for a crop grower, the SBR revenue is maximized when irrigation needs are removed in peanuts and there is a reduction in pest and disease control costs (Table 3 6, scenario B3a). How attainable are net revenues increases through the adoption of SBR in the South eastern US? Mixed crop livestock systems in the Southeastern US have great potential for revenue increases via the adoption of SBR (Franzluebbers, 2007). Furthermore, SBR has the potential to boost revenues in farms that grow peanuts and cotton and already have livestock, as described above. In other words, SBR benefits can be attained in farms with investment in livestock while also managing crops and livestock in a non integrated fashion. For cotton peanut farmers who do not own cattle and are not interes ted in performing cow calf operation, the SBR will resu lt in higher economic benefits if conservation tillage practices are not yet incorporated in their respective systems. pote ntial (Chapter 2), which found farmers willing to adopt the SBR in the coming three years, especially those that had both cattle and crops in their farms and that utilized intensive tillage methods. These are the farmers who, from an economic perspective, might obtain the greatest benefits from the adoption of the SBR. In the same sense, of the farmers unwilling to adopt the SBR , 22 % cited their main reason for not planning to adopt the SBR as lack of interest in incorporating cattle into their crop farmin g systems and 16% as the same absence of interest for the inverse incorporation. Thus, both the
72 adoption trends and economic analyses confirm the potential of SBR to improve economic benefits in peanut cotton cropping and livestock farms. Nonetheless , the question remains ; how many farms with crops and livestock are in the region? While the second half of the 20th century saw the emergence of separate and specialized farming operations (of either livestock or particular crops) amongst large scale US agricu ltural producers in the US (Gardner, 2002), some small scale farms during this same period (and through to today) maintained both operations, although these were typically not fully integrated in a single rotation. For instance, in many of these small scal e farms , livestock is not fed with these crops and instead largely rely on purchased feed; likewise, livestock are only beneficial for crops when their manure is used as fertilizer (MacDonald et al. , 2013). According to the survey of farme rs in the region regarding SBR adoption potential (Chapter 2), 53% of the farmers have both crop and livestock operations; this is far above national average of 27% (MacDonald et al. , 2013). This could indicate that, in terms of potential number of farms where the adoption of SBR might be translated into significant improvements in farm revenues, the SBR has a potential audience in the region. However, the existence of farms with both crop and livestock operations is only one of other conditions that might limit or convers ely catalyze the realization of the the Chapter 2 , as well as recent economic analysis on the structural changes in US agriculture based on data from the ARMS and Census o f Agriculture ( i.e. Mac Donald et al. , 2013), illuminate other possible aspects that could play a critical role in the realization of the economic benefits of SBR in the region:
73 Extension service: In considering the existence of potential SBR adopters in t he region and the known potential of this farming system to boost peanuts and cotton yields, the role of agricul tural extension is crucial. In C hapter 2, results showed that potential adopters first learned about the SBR through a cooperative extension ser vice visit to their farms.. Consequently, it is expected that the adoption of SBR and subsequent yield and revenue increases in farms would depend on the active role of the extension service that accompanies the adoption process and ensures the proper impl ementation of conservation tillage practices in order to increase yields. Correct implementation of these practices and of agronomic practices in general could generate consistent increases in yields avoiding variable crop yield responses to better tillage practices as reported in previous studies. For example, results from Hartzog and Adams, 1989; Grichar, 1998; Johnson et al., 2001; and Jordan et al., 2001, show variable peanut yield response to conservation tillage due to management problems related to w eed control, diseases, hardpan, pod digging and weather conditions. Yield gap reduction: after peanut quotas were eliminated in 2002 and peanut production was incorporated into the broad based commodity program support, the peanut yields increased by 21% (Mac Donald et al. , 2013). In spite of this increase, Table 3 2 shows the wide difference between the yields reported for the Frutiful Rim region for the past four years and the realized yields in the long term experiment at UF NFREC during the same period . An averaged difference of 635 and 2,683 kg ha 1 in cotton and peanuts, respectively, is large enough to demonstrate the need to improve management practices in cotton and peanuts farms. Previous studies have mentioned that weed control issues, diseases , and pod digging problems explained the heterogeneous response of peanut yield to conservation tillage (Hartzog and Adams, 1989; Grichar, 1998; Johnson et al., 2001; Jordan et al., 2001). SBR can result in higher net revenues for farmers so long as the yi eld potential is realized by farmers and there are external aids to facilitate the adoption of conservation tillage (and other practices) to reduce yield losses. Conservation vs. conventional tillage: No till practices have not yet being widely adopted. Th ese practices accounted for nearly 35 percent of planted acreage for main field crops in US including cotton (Horowitz et al. , 2010). Similarly, in the Southeastern region, the survey implemented for the adoption study (Chapter 2) showed that 63 % of total respondent s keep practicing con ventional tillage. Also , farmers willing to practice SBR in the next three years are mainly using conventional tillage practices. This may be explained by the likely benefits of conservation tillage on reducing labor needs (G ardner et al. , 2009) and the positive impacts of these practices on productivity of peanuts and cotton (as demonstrated in the long term experiment at UF NFREC, and in other studies; e.g. White et al . , 1962; Dickson and Hewlett, 1989; Norden et al., 1977; Johnson et al., 1999). In short, the potential benefits of adopting conservation tillage in conventional till farms m ay incentivize SBR adoption. In this sense, SBR has potential to boost peanut cotton farm revenues as many farms have not yet
74 benefited fro m the improvement in yields achievable by conservation tillage practices. Risk perception and management: Farmers that receive government payments perceive lower risk in changing practices and consequently consolidate their croplands more rapidly (Key and Roberts, 2007a and 2007b). However, most of the government payments are received in the Plains, Corn Belt and the Delta were there are already higher crop yields. In contrast, Florida only receive d about 0.9% of the national USDA subsidy payments between 1 995 and 2012 (EWG, 2014), which can lead to a higher risk perception amongst farmers of changed practices and hence less probability of moving toward specialization (Key and whil st still not receiving any form of government payment, might be incentivized to adopt more diversified systems like the SBR and consequently might benefit from associated higher revenues (as shown in the adoption potential model in C hapter 2). These mi xed crop livestock systems could : reduce financial risk of relying on limited numbers of single crops; reduce incidence of pest and disease; and improve soil quality (Russell et al., 2007; Kastvairo et al. , 2006). In addition, farm ers may actually gain greater resilience to: price fluctuations through increased yields, which could offset drops in market prices and unanticipated drought conditions by reducing irrigation needs. In sum, the SBR has high potential to increase economic benefits to cotton peanut prod ucing farms, especially those that have livestock, practice conventional tillage and do not receive government payments. However, these benefits will be a ttainable only as conservation tillage as well as crop and livestock integration practices are adopted ; if undertaken, these may boost the yields and close the apparent yield gap occurring in the region. The extension service would also play a crucial role in realizing the full benefits of the SBR in the Southeastern US. For farms under conventional cotton peanut rotation already using conservation tillage practices, additional economic incentives may be needed to cover the opportunity cost of integrating perennial grasses into their rotation. The recently reauthorized E nvironmental Q uality I ncentives P rogr am (EQIP) to provide financial and technical assistance to farmers for implementing conservational practicas can be an opportunity for these farms.
75 Summary This profitability analysis indicates that SBR is more profitable in the long term than the conventi onal peanut cotton rotations using intensive tillage practices. For instance, for small peanut cotton producers this study showed that SBR can generate important increases in net revenues (in the range of US$278,000 to $490,000 over ten years) relative to the conventional peanut cotton rotation that uses conventional tillage practices. Comparatively higher net revenues with SBR are explained by the remarkable enhancement in crop yields (almost double), when cotton and peanuts are rotated with bahiagrass. Hi gher crop yields resulting from SBR compensate the reduction this system entails in regards to land availability for crops, given that part of the land is required to be allocated bahiagrass cultivation. The net revenue can be further maximized in a SBR if the bahiagrass is used to produce hay. Furthermore, using this system, irrigation needs in peanuts cropping can be reduced without limiting yield and under environmental conditions with sufficient precipitation; and additional cost reductions can result f rom decreased need for pest control. Such findings are supported by Katsvairo et al. (2007) as well as by records from the long term experiment at NFREC (as used in this analysis). Also, this system is more profitable for farmers that own non integrated c rop and cow calf operations. When conservation tillage practices and a sod are fully integrated into the cotton peanut rotation by farmers who own cattle, the net revenue over 10 years is further increased compared to a farm growing these crops and raising cattle separately. This increase is due to an additional enhancement of yield when peanuts and cotton are grown in land previously used to grow bahiagrass. Additionally, the usual negative revenues from the cow calf operation in the region (when total cos ts are
76 included in the economic analysis) are reduced due to increases in weight at calving and in stocking rates as observed in the UF NFREC experimental station in Marianna, Florida (D. Wright, personal communication, 2014) . Although the net revenue abov e total costs in cow calf operation remains negative, the total revenue for the whole system is still comparatively much higher using SBR. Increases in crop yields in this crop pasture rotation have been further explained by the recycling of nutrients in t he system, allowing crops to benefit from the greater residual fertility left by cattle grazing (George et al. 2013). Similar to the results obtained for a crop grower, SBR revenues are maximized for peanuts, as their irrigation needs are removed; likewise , SBR use results in reduced pest and disease control costs.
77 Table 3 1. Rotations considered for the multiyear linear programming modeling. Cotton grown after bahiagrass), Bahia1 (Bahiagrass year 1 Establishment), Bahia 2 (Bahiagrass year 2). Â§Planting and harvesting months, respectively: Peanut: May Oct; Cotton: May Oct; Bahiagrass: Mar Dec. Table 3 2. Cotton and peanut yields used per l and use scenario Yield ( kg ha 1 ) Scenarios for a peanut cotton farmer Scenarios for a peanut cotton farmer with cow calf operation Scenario A1 Scenario A2 and B2 Scenario A3a Scenario A3b Scenario A3c Scenario B3 Scenario B3a Peanut cott on rotation with convention al tillage Peanut cotton rotation with strip tillage Sod based rotation with bahiagrass for contract grazing Sod based rotation with bahiagrass hay production A3b+ reduction in chemicals and irrigation costs Crop livestock operat ion integrated in a sod based rotation + Reduction in chemical and irrigation costs Cotton 887 1383 1606 1493 1493 1606 1582 Peanut 4070 6506 6878 6878 6690 6878 6690 Source: United States Department of Agriculture Economic Research Service (2014) . Averaged yield data from 2010 to 2013 in the Fruitful Rim region. http://www.ers.usda.gov/data products/commodity costs and returns.aspx#.U4IGsvl5O_E Yield data collected from 2010 to 2013 at the long term sod based rotation experiment in Quincy, FL, and provided by the North Florida Research and Education Center. Year Â§ Sod based rotation (SB) Conventional rotation Cow calf operation SB1 SB2 SB3 SB4 CV LV 1 Peanut Cott on Bahia 1 Bahia 1 Cotton Cow calf operation alone 2 Cotton Bahia 1 Bahia 2 Peanut_B Cotton 3 Bahia 1 Bahia 2 Peanut_B Cotton_B Peanut 4 Bahia 2 Peanut_B Cotton_B Bahia 1 Cotton 5 Peanut_B Cotton_B Bahia 1 Bahia 2 Cotton 6 Cotton_B Bahia 1 Bahia 2 Peanut _B Peanut 7 Bahia 1 Bahia 2 Peanut _B Cotton_B Cotton 8 Bahia 2 Peanut_B Cotton_B Bahia 1 Cotton 9 Peanut_B Cotton_B Bahia 1 Bahia 2 Peanut 10 Cotton_B Bahia_1 Bahia 2 Peanut_B Peanut
78 Figure 3 1. Rotation alternatives assessed for A ) a cotton and peanut grower a nd, B ) for a cotton and peanut grower who raise cattle. Cotton peanut grower and cattle rancher Conventional tillage Conventional cotton peanut rotation and calf operation (SCENARIO B1) Strip tillage Conventional cotton peanut rotation and calf operation (SCENARIO B2) Cotton peanut bahiagrass integrated in a sod based rotation (SCENARIO B3) Scenario B3+ without irrigation and reduction on chemicals for pest and disease control (SCENARIO B3 a) Cotton peanut grower Conventional tillage Cotton peanut rotation (SCENARIO A1) Strip tillage Cotton peanut rotation (SCENARIO A2) Sod based rotations with bahiagrass under rent for grazing (SCENARIO A3 a) SBR with bahiagrass for hay production (SCENARIO A3 b) Scenario A3 b + without irrigation and reduction of chemicals for pest and disease control (SCENARIO A3 c) A. B.
79 Table 3 3. Total production costs and prices for peanuts and cotton used in the analysis Item Peanuts Cotton Price received per crop product (per kilogram) 0.55 2.46 Total costs (dollars per hectare) Conventional tillage 2420.92 1958.66 Strip tillage 2383.15 1928.45 Strip tillage rain fed 1854.18 2010 2013 average p rices reported by the Economic Research Service USDA, Commodity Costs and Returns. 2010 2013 average total co sts for peanuts and cotton . Include variable and fixed costs. Fixed costs include general overhead ; machinery depreciation, housing, insurance and taxes; and farm land cost, taxes and cash payments (Source: University of Georgia Cooperative Extension Ser vice)
80 Table 3 4. Total production costs and prices for bahiagrass and cattle management used in the analysis Item Bahiagrass Cattle (cow calf operation) Total costs Cow calf operation (dollars per cow per year) 1046.36 772.43 Â§ Bahiagrass establishment (dollars per hectare) 699.08 Bahiagrass maintenance (dollars per hectare) 438.25 Bahiagrass for hay production (dollars per hectare) 1022.51 Â¶ Revenue sources Hay (dollars per ton) 127.5 0 # Gross value of production per co w 707.86 734.95 Pasture Land Rent Value (dollars per hectare) 54.96 Â§Â§ Cattle stocking rate (cow calf pairs per hectare) 1.5 0 Â¶Â¶ 2.2 0 Hay production (tons per hectare) 9.8 0 ## Clemson Cooperative Extensio n Service (2014). ( http://www.clemson.edu/extension/aes/budgets/ ) calf operation, where this operation is not integrated into a c rop rotation. This is the cow calf production costs per cow estimated based on budgets develop by UG CES and adjusted for a stocking rate of 1.5 cow calf pairs per hectare. ( http://www.ces.uga.edu/A griculture/agecon/ ). Â§ Total cost per cow in a cow calf operation integrated into the sod based rotation. The value is estimated based on budgets developed by UG CES and adjusted for a stocking rate of 2.2 cow calf pairs per hectare, an increase of 50 lb . at calving per animal, and replacing the cost of harvested feed by the cost of establishing and maintaining the bahiagrass sod. Â¶ Based on the cost of establishment of bahiagrass for hay production reported by Alabama Cooperative Extension Service (200 8) # Average prices received by farmers in 2010 2013 (Economic Research Service, 2014) Cow calf production gross value of production per cow estimated for a conventional cow calf operation based on a stocking rate of 1.5 cow calf pairs per hectare and UG CES budgets. calf production gross value of production per cow estimated using UG CES budgets and adjusted by incorporating an increase of 50lb per animal at calving in the SBR and a stocking rate of 2.2 cow calf per hectare. Â§Â§ Land rent value ba sed on average price reported for Northeast and Northwest Florida by NASS for 2010 2013 ( http://quickstats.nass.usda.gov/ ) Â¶Â¶ Based on usual stocking rates in the area reported by Katsvairo et al. (2006) and George et al. (2013) and s tocking rate achieved in a SBR experiment with cow calf operation in Marianna, Fl. (George et al., 2013) and D. Wright personal communication, 2014) ## Source: Alabama Cooperative Extension Service (2008) http://www.aces.edu/agriculture/business management/budgets/
81 Table 3 5. Economic benefits of conventional vs. sod based rotations alternatives for a peanut cotton grower Scenario A1 Scenario A2 Scenario A3 a Scenario A3 b Scenario A3 c Convention al peanut cotton rotation with convention al tillage Conventional peanut cotton rotation with strip tillage Sod based rotation with bahiagrass for contract grazing Sod based rotation with bahiagrass h ay production Scenario A3 b + reduction in chemical s and irrigation costs Net present value of returns (USD) 67,156 873,264 345,516 497,148 564,327 Marginal net income as compared with Scenario A1 (USD) ---806,108 278,360 429,991 497,171 Marginal n et income as compared with Scenario A2 (USD) ------527,748 376,117 315,832 Net income from peanuts (USD) Â§ 44328 290,346 279,193 279,193 363,378 Net income from cotton (USD) Â§ 129 , 332 838,726 388,249 343,424 348,457 Net income from bahiagrass yea r 1 (USD) Â§ ------143,429 17,309 17,309 Net income from bahiagrass year 2 (USD) Â§ ------69,831 43,214 43,214 Total area grown in the 10 yr period (hectares) Peanuts 240 240 200 200 200 Cotton 560 560 200 200 200 Bahiagrass -----400 400 400 Accumulated net income from the whole rotation over 10 years Increase of net revenue in Scenario A2 compared to A1 is mainly due to enhancement of crop yields when strip tillage practices are incorporated. These yield increases were not ed under experimental conditions. This reflects the yield gap existing in the region as scenario A1 yield data used in the analysis were those reported by USDA in farms of the region (versus yields attained with strip tillage in the UF NFREC experimental s tation at Quincy, FL) Â§ Accumulated net income from each activity in the rotation over 10 years
82 Table 3 6. Economic benefits of conventional vs. sod based rotations alternatives for a peanut cotton and livestock farmer Scenario B1 Scenario B2 Scenario B3 Scenario B3a Conventiona l peanut cotton rotation with conventional tillage and cow calf operation Conventiona l peanut cotton rotation with strip tillage and cow calf operation Crop livestock operation integrated in a sod based rotation Scenario B3 +_ reduction in chemical and irrigation costs Net present value of returns (USD) 77,217.35 270,063 431,148 491,414 Marginal net income as compared with Scenario B1(USD) ---347,281 508,366 568,632 Marginal net income as compared with Scenario B2 ( USD) ------161,085 221,351 Net income from peanuts (USD/10 yr) Â§ 22164 179,727 279,193 363,378 Net income from cotton (USD/10 yr) Â§ 64666 373,291 388,249 383,849 Net income from bahiagrass year 1 (USD) Â§ ------155,667 155,667 Net income from b ahiagrass year 2 (USD) Â§ ------62,068 56,849 Net income from cow calf operation (USD) Â§ 203 , 110 203 , 110 ------Total area grown in the 10 yr period (hectares) Peanuts 120 120 200 200 Cotton 280 280 200 200 Bahiagrass 400 400 400 400 Accumulated net income from the whole rotation over 10 years Increase of net revenue in scenario B2 compared to B1 is mainly due to enhancement of crop yields when strip tillage practices are incorporated. These yield increases were noted under experi mental conditions. This reflects the yield gap existing in the region as scenario B1 yield data used in the analysis were those reported by USDA in farms of the region (versus yields attained with strip tillage in the UF NFREC experimental station at Quinc y, FL). Â§ Accumulated net income from each activity in the rotation over 10 years
83 CHAPTER 4 EFFECT OF CONSERVATION AND CONVENTIONAL TILLAGE ON RUNOFF AND SOIL, NITROGEN (N) AND PHOSPHORUS (P) LOSSES IN A POTATO BASED MIXED CROP LIVESTOCK SYSTEMS IN COLO MBIA Overview of Research Problem Loss of soil and nutrients due to water erosion is one of the most important factors that explain reductions in soil fertility and crop yield potential in agricultural fields (Castro Filho et al., 1991; Hernani et al., 199 9; Lal, 1984). Apart from loss of nutrients and s oil , water erosion also affects crop productivity by deteriorating soil properties important for crop growing, such as organic matter, soil aggregation, and water infiltration (e.g. Langdale & Shrader, 1982; Larson et al. , 1983). Water erosion not only affects agricultural productivity and subsequently, farm profitability, but also has deleterious effects on the environment. Nutrients lost from agricultural areas, as a result of water erosion, may be dissolve d in the water or absorbed by runoff sediments (Barbosa et al., 2009; Bertol et al., 2007) causing environmental problems such as silting and eutrophication of water bodies where these eroded materials are eventually deposited (Pote et al., 1996). Of the n utrients lost from cultivated lands, nitrogen and phosphorus are the most significant from an environmental perspective, as these constitute the major cause of water eutrophication in agricultural landscapes (Sharpley et al., 1987). Phosphorus is of parti cular pertinence because, despite its low solubility, it can trigger the eutrophication of water resources even at relatively low concentrations because it becomes totally available to organisms in aquatic systems (McIsaac et al., 1995). For this reason, a voiding its loss through water erosion is fundamentally important to conserving water quality in agricultural landscapes.
84 The main factors that influenced water erosion are rain, soil, relief, soil coverage and soil management practices. Soil cover and soi l management and preparation also affect the amount of nutrient s lost in runoff, as do: the amount of nutrients in the soil at the origin of the sediment; the quantity and application method of fertilizer; and the rain volume (Schick et al., 2000; Bertol e t al., 2007; Withers et al., 2001; Daniel et al., 1994). Due to the impact of management practices on the loss of soil and nutrients, scientists (and the public more broadly) are increasingly drawn toward soil and agronomic practices that seek to conserve soil, utilize nutrients more efficiently and reduce runoff and thereby reduce negative impacts of water erosion and nutrient loss (Choudhary et al., 1997). Conservation tillage (CT) is a practice that has been adopted to reduce soil and nutrient loss due to water erosion, by reducing run off and increasing infiltration (Lal et al., 1994). This practice is generally effective at reducing erosion and run off for the critical period between planting and canopy development (Hansen et al., 2000). Additionally, there are other practices that are reported as effective in reducing sediment and nutrient loss from agriculture. These include: the inclusion of pasture in crop rotations in conjunction with CT (e.g. Ernst and Siri Prieto, 2009); terracing, grass strips, strip cropping and contouring (Muller et al., 1981); and the construction of physical structure (e.g. Cullum et al., 2006). loss of soil (and in some cases water) compare this criterion the tillage system must leave at least 30% unincorporated plant residue. Additionally, it is desirable to leave the soil surface as rough as possible (Mueller et al.,
85 1981). CT practices can include no tillage or minimum tillage practices along with different types of crop residues and cover crops. The impact of these practices o n nutrient transfer from cultivated lands can be measured in terms of: the nutrient concentration in the water runoff and s ediments; or as total nutrient lost (i.e. the sum of nutrients in solution and sediments) (Hansen et al., 2000).As sediment loss and runoff volume are reduced with CT practices, so might be expected to drop, the total loss of nitrogen and phosphorous. This has been confirmed in various previous studies. Choudhary et al. (1997) found that soil erosion and surface runoff were lower in no till plots of continuous corn r elated to mo ldboard plo w ing and chisel plowing tillage methods. Meanwhile, Baker and Laflen (1983) recorded 75 90% reductions in soil erosion for n o till compared with that of mo ldboard plo w ing. Also, Zheng et al. (2004) reported on average, no tillage caused an approximate 25% decrease in runoff compared with conventional systems. Packer and Ham ilton (1993) found that in Australia, runoff and sediment loss was significantly reduced in treatments which adopted minimal soil disturbance compared with traditional tillage; likewise Ruiz et al. (2011) noted that in steep vineyards, rows with cover crop s lost between 50% and 75% less soil than the rows with traditionally tilled soil. Similarly, in Georgia (USA), Langdale et al. (1992) found runoff coefficients on conservation tilled soils cultivated with clover ( Trifolium incarnatum ) and grain sorghum ( S orghum bicolor ) of only 6%, compared to 35% for those conventionally tilled. Also, the same authors found rill and interrill soil loss rates also reduced significantly due to the surface residue provided with CT. These results are explained by the reductio n of raindrop impacts on bare soil that normally cause soil aggregates breakdown (and thereby more transportable particles
86 and micro aggregates). These particles can slow infiltration rate to produce increased runoff and soil erosion. Although CT practice s can reduce runoff and soil erosion (and therefore total nutrient losses), this does not mean that they can also reduce the concentration of nutrients in water and sediments relative to conventional tillage. Actually, these concentrations can indeed incre ase with crop residues; this is due to resultantly high level of nutrients in the superficial soil layer (resulting from the accumulation of nutrients on the surface after their liberation from the residues by rain and by residues decomposition) (Burwell e t al., 1975). Also, in no till systems the combination of the application of fertilizers on the soil surface and their reduced mixing with deeper soil, serves to increase nutrient status in the topsoil layer (Bertol et al. , 2007, Hansen et al. , 2000). Ther e have been reports of greater runoff nutrient concentrations under soil conservation management systems than those concentrations resulting from conventional tillage. For example, Bertol et al. (2007) found that, in the highlands of Santa Catarina (Brazil ), soybean plots with different tillage systems had greater phosphorous losses in runoff water under no till treatments than those plots under conventional management. Similar results were shown by McDowell and McGregor (1984) who found that CT in north Mi ssissippi (US) produced greater nitrogen and phosphorus concentrations in runoff and sediments than those plots with conventional tillage. In Colombia, the regional environmental authority ( Corporacion Autonoma Regional CAR) and the international German c ooperation ( Deutsche Gesellschaft fÃ¼r Internationale Zusammenarbeit GI Z ) started ado pting and promoting in 1999 the
87 principles of CT in potato based systems in the Andes. Since then the CAR, via extension activities, has continue d supporting the adoptio n of these practices in order to reduce soil and nutrient losses in the Fuquene Lake watershed (Quintero and Comerford, 2013). Concern relating to the accelerated eutrophication of the Fuquene Lake, caused primarily by the impact of potato based systems on water quality (Rubiano et al. , 2006), has prompted a significant government investment in the extension activities in this watershed. The specific CT practices promoted in these potato based rotations are: reduced tillage and incorporation of a cover crop prior to potato sowing. Crop resides are left unincorporated. The CT system in potato based systems in this watershed (as in many other areas dedicated to the potato ( Solanum tuberosum ) production in Colombia) is a potato pasture rotation, where 2 3 yea rs of potato cultivation are followed by 2 3 years of ryegrass ( Lolium perenne ) grazing. To cultivate potatoes, soil is prepared conventionally using intensive tillage (where the soil is inverted with conventional plowing followed by rotovator passes).When land is in pasture, some grazing occurs and any fertilization or tillage practice (to loosen the soil) is practiced. With CT the resultant system is a mixed crop livestock system in steep lands, improved by the increase in soil organic matter through crop covers, and the enhancement of soil structure as a result of less intensive tillage systems. In this particular system, Quintero and Comerford (2013) have already reported important enhancements of soil organic matter in soil aggregates with CT, when com pared with conventional tillage, and these were associated with reductions in bulk density and increases in soil porosity (Quintero, 2009). Howe ver, the impact of
88 incorporating CT practices on nutrient and soil losses in this mixed crop livestock system ha s not yet been investigated. The existing methods and models for predicting and controlling runoff and erosion have been empirically derived for temperate gentle sloping areas (El Swaify, 1997). This implies that field experiments are needed to anticipate the impacts of practices with a likely positive effect on soil erosion and runoff (and nitrogen and phosphorous losses) in order to evaluate the effectiveness of conservation practices in steep tropical lands. To investigate the effects of CT in rotation s that include root crops, such as potatoes, the field experiments must be designed so as to evaluate the entire rotation (Carter, 2001). The reason for this is that, in potato based rotations, tillage and management practices are not constant across crops in the rotation. In consequence, studies of the entire rotation would permit analysis of the interactions between the mix of crops, types of tillage and other differences in land management (Keller, 1989; Carter, 1994). Thus, the objective of this study w as to evaluate the impacts of conservation tillage on runoff and sediment, nitrogen, and phosphorous losses over three crop cycles. This objective r elied on two hypotheses: 1) that total runoff and soil losses are reduced with CT so the total losses of nit rogen and phosphorous are lower ; and 2) , that the nitrogen and phosphorus concentrations in runoff and sediments are higher in CT versus intensive tillage ( IT ) .
89 Methods Study Site This study was conducted at experimental sites established in the municipal ities located in the upper Fuquene Lake watershed; north of Bogota, Colombia . The average monthly temperatures do not vary greatly throughout the year and the mean annual values are between 12 Â°C and 18Â°C. The mean monthly relative humidity varies between 70% and 80%. The annual mean precipitation is 845 and 899 mm in site 1 and 2, respectively (CAR, 2014).The experimental sites were located at 2,900 m above sea level (site 1) and 2,500 m above sea level (site 2) . The soil type at site 1 was an Andosol classified as Typic Hapludands (IGAC, 2000) and characterized by high organic matter contents (9 12% ) , low bulk densities (0.85 1.10 g cm3) , deep A horizon (70 130 cm depth) , and a sandy clay loam texture. Site 2 was an Inceptisol classified as Typic Haplustepts (IGAC, 2000), with a clay texture, lower composition of organic content (3%) , and higher bulk density than site 1 (1 ,39 1,53 g cm 3 ) . See Appendix C for a description of a typical soil profile from each study site. Field methods and experimental design Field trials were located on grazed and non fertilized ryegrass plots that had not been pl owed for the last 3 5 years. A completely randomized block design with three replications in each site was used. Potato pasture rotations with IT and CT were established from August, 2011 to February 2013. The potato varieties used in the experiment were Parda pastusa in rotation phase 1 (crop cycle 1); and Bettina in rotation phase 2 and 3 (crop cycle 2 and 3). Two different sequences of conventional
90 and CT rotations were established. One of the IT consisted of two potato cycles (with intensive til lage practices),followed by one ryegrass rotation (IT1) while the other IT comprised two ryegrass cycles followed by one potato cycle (IT2). The CT rotations were: i) first rotation cycle with reduced tillage potato, second cycle with oats and third cycle with potato sown into oats residues and reduced tillage practices (CT1); and ii) first cycle with oats, followed by reduced tillage potato and returning to oats in the third rotation cycle (CT2). With these designs all crops of the rotations were present a ll years allowing comparisons across crop types. These rotations are illustrated in T able 4 1 and the ir respective planting and harvesting dates. CT and IT treatments mainly differed in three ways: i) CT d id not invert the soil and employs comparatively le ss machinery passes for soil preparation, while IT used more machinery passes, including those of a rotovator that highly disrupt soils structure; ii) CT integrate d oats as cover crop in the rotation, which leaves residues on the soil surface for subsequen t potato sowing; and iii) both CT and IT appl ied the same total amount of fertilizer, nonetheless, each technique applie d these in differing proportions in each cycle. Intensive tillage practices in potato cultivation (potato IT) consisted of using one pa ss of a rotovator to 15 20 cm of depth and one pass of a tractor drawn furrow opener. When the potato was planted in the first phase of the rotation, a chisel plow was also used to loosen the compacted soil of the degrading ryegrass. Two fertilizer appli cations, split evenly, were used to apply N, P, and K at a total of 195, 375, and 90 kg ha 1, respectively. In potato plots with CT (potato CT) two passes of a tractor drawn furrow opener were undertaken similarly as in IT, one chisel plow pass was used i n the
91 initial stage of the rotation to loosen previously pastured soils. The fertilization rate, though the same as in potato IT, was split differently within the two fertilizer applications, 25% was applied at planting and 75% some 45 days after planting, with the purpose of avoiding mineral nutrient losses at the beginning of the crop cycle when the canopy is not fully developed. Oat cover crop used one pass of straight blades rotovator. When planted at the initial stage of the rotation one pass of the c hisel plow was used (as for potato IT and potato CT) and fertilizer was applied at a rate of 75 k g ha 1 of N:P:K containing sulfur (31:8:8:2) at planting and 75 k g ha 1 45 days after planting. Oats of crop cycle 2 and 3 were not fertilized as they were pla nted in plots previously used for potato cultivation. The oats therefore relied on the residual fertilizers left by this crop. Ryegrass in the rotation was grazed by sheep at a stocking rate of 0.7 animal unit (AU) . In each crop plot, a runoff plot was ins talled which consisted of a metallic frame with vertical borders (2 m x 5m) to collect and channel surface runoff from natural rainfall and capture soil losses. The metal structure was inserted into the soil to a depth of 10 cm to avoid leakage; other 10 cm of the metal borders were left above soil. All runoff plots were with a 26 30% slope. At the bottom of each plot, a galvanized metallic structure with the same width of the runoff plot was installed to collect runoff and soil losses by water erosion. The runoff water was channel to a pipe that transported the water to a 220 l bucket. This runoff container was emptied weekly or more often in case it was already filled up with water during high rainfall events (see Appendix D with a diagram of this structur e). Also, the level of the runoff in the container was register ed to estimate the total runoff volume per sampling event. A runoff sample was collected for
92 each plot every week or before (if high runoff occurred) and stored at 4Â°C until analyzed for conce ntration of ammonium N, nitrate N and phosphate. At the same time, soil captured in the metallic structure at the bottom of the runoff plot was collected and weighted in the field. A soil sample of 500 gr was collected for water content and nutrient concen trations analysis in the laboratory. Lab methods Chemical determinations of phosphorous and nitrogen in runoff water and sediments were conducted in the lab at the International Center for Tropical Agriculture (CIAT) in Cali, Colombia. Phosphorous was ext racted from sediments by the Bray II method; while concentrations were determined in sediments and water using the phosphate molybdenum reaction and ascorbic acid by colorimetry (John, 1970). Ammonium and nitrate were extracted from soil using potassium ch loride; their determination in runoff and sediments used the modified Berthelot method for ammonium and the cadmium reduction method for nitrate (Keeney and Nelson, 1982). Data analysis Nutrient and sediment losses were calculated as the product total s oil and runoff water losses and the respective nutrients concentrations. Analysis of variance (ANOVA) was performed to determine crop type effects within each crop cycle on runoff water and soil losses, and on nitrogen (ammonium N and nitrate N) and phosph orus concentrations and total losses. Also, to understand if these losses and nutrient concentrations were concentrated in specific events, analysis of variance was carried out to understand the effect of sample event on these variables, and if during thos e events there was an interaction with crop type. When crop type and sample event had a
93 significant effect, the Duncan multiple range test was used to separated means (P < 0.10). Results Rainfall and run off Precipitation varied between 235 438 and 314 431 mm in site 1 and 2 respectively; and in both sites the precipitation in the third crop cycle was less than in the previo us two crop cycles. Average run off observed in the different plots was a low percentage of total rainfall. In sites 1 and 2, high er quantities of runoff were measured in ryegr ass plots which accounted for 7 and 13% of the rainfall, respectively. Rainfall amount and average runoff values per cycle and cover treatment are given in F igure 4 1. Andosols Surface runoff volume and sedimen ts Analysis of variance for runoff per i ndividual sampling event and for the whole crop cycle indicated that crop type had a significant effect during some crop cycles but not on others (Table 4 2). The runoff water measured per sample event was higher in ryegrass and potato IT in the crop cycle 1 and 2, respectively (crop by cycle interaction, p = 0.010). The average accumulated total water volume lost by runoff at the end of each crop cycle was significantly different between crops in the crop cycle 2. Whereas in crop cycle 1 and 3 the surface runoff volume was statistically similar across crops (cycle effect, p=0.012). In crop cycle 2, potato IT produced the highest losses of water, followed by ryegrass (Table 3 2).
94 It is worth mentioning that higher w ater losses caused by runoff in ryegrass and potato IT were measured during specific events that occurred in crop cycles 1 and 2. In these events, surface runoff was significantly different from measurements in the other weeks (sampling event effect, p < 0 .05 ) (Table 4 3). Sediments originating from water erosion were significantly higher in potato IT during crop cycle 2 and these did not differ statistically from losses observed in potato CT (Table 4 4). These high soil losses were produced in a single m easurement event (crop x sam pling event interaction, p < 0.05 ). Although soil losses were not influenced by the cover in crop cycle 1 and 3, the average soil loss tended to be higher in potato IT as well as in potato CT during the first cycle. Concentrati on of nitrate N, ammonium N and phosphorus in runoff water and sediments The means of nutrient concentration at each measurement event were only different across crop types during crop cycle 1 for nitrate N and crop cycle 2 for nitrate N and phosphate. Th ere were not significant differences in ammonium N concentrations in runoff water produced in the different crops during any crop cycle. The concentration of nitrate N was significantly higher in potato IT and potato CT during both cycles (1 and 2) than in oats and ryegrass. The concentration of phosphates was significantly different across crop types during crop cycle 2, with higher levels in runoff water from plots with ryegrass compared to plots with potato CT. The phosphate concentration in runoff water from potato IT and oats was not significantly different from the concentrations found in the runoff water from ryegrass plots (Table 4 5). Phosphate concentrations during crop cycle 3, though not significantly different, exhibited high values in potato IT and potato CT during specific measurement events (sample event effect, p = 0.045).
95 With respect to nutrient concentrations in sediments, the crop type did not have a significant effect on ammonium N and phosphate concentrations. Instead, nitrate N concent ration was higher in potato IT during crop cycle 1, followed by the concentrations measured in sediments from plots with potato CT. The concentrations of nitrate N in potato IT and potato CT were not statistically different (Table 4 6). Although, ammonium N concentrations were not significantly different in runoff from plots with different crops during crop cycle 2, some high concentrations were measured in a specific sampling event, and in which potato CT and potato IT presented the highest values (signifi cant cover x sample interaction at p<0.05). Total nutrient losses in runoff water and eroded soil Total ammonium N losses were significantly different across covers during the second crop cycle. The total ammonium N losses were higher in potato IT and sig nificantly different from other land covers including potato CT (Figure 4 2). Total losses on ammonium N were confounded by the differences in total surface runoff from crop types instead of by differences in ammonium concentrations (which were not signifi cantly different among covers). As illustrated in Table 4 2, runoff from potato IT was the highest during crop cycle 2. Similarly, although surface runoff volumes in ryegrass were also high during the same crop cycle, their lower ammonium N concentrations caused a compensatory effect resulting in significantly lower total losses of this nutrient compared to the losses found in potato IT. Potato with IT and CT presented higher total nitrate N losses during crop cycle 1 and 2, in comparison to results for oa ts and ryegrass (Figure 4 2). This is explained by higher concentrations of this nutrient in runoff water from plots cultivated with potato under the two tillage treatments, as well as by high runoff volume. It is worth nothing
96 that although runoff from po tato CT plots was lower in the second crop cycle than the measured runoff from potato IT, it was not low enough to compensate for the high nitrate N concentrations. This situation, along with statistically similar nitrate N concentration found in potato IT , resulted in similar total losses of this nutrient in potato with conventional and intensive tillage. In contrast, high runoff in ryegrass was offset by lower nitrate N concentrations, producing lower total losses of this nutrient relative to potato treat ments. Total phosphorus losses, resulting from surface runoff water, were significantly different across covers in crop cycle 3; these were higher in potato CT. Although total losses of phosphate in potato IT and oats were lower, these were not statistica lly different from those measured in potato CT. Total losses in ryegrass were lower and different to those measured in the other covers (Figure 4 2). High phosp hate concentration found in run off water from potato CT and potato IT during crop cycle 3 expla ins the higher phosphate l osses in these crops. Lower run off during this crop cycle did not compensate for those high phosphate concentrations. With respect to total nutrient losses in sediments, cover type did not have a significant effect on total ammoni um N losses in any of the three crop cycles (Figure 4 3). Total nitrate N losses were only significantly different across crop types during the first cycle, these being higher in potato IT than in losses for the rest of covers. Nitrate N total losses in po tato CT, although lower, were not statistically different from losses in potato IT. Similarly, losses of nitrate N in potato with CT and IT are explained by their similar nitrate N concentration and soil losses during crop cycle 1. In contrast, these conce ntrations were lower in oats and ryegrass runoff. Total P losses among covers
97 were only different during crop cycle 2; of these, potato IT had the highest losses. Potato CT and oats have lower P losses but the respective average was not statistically diffe rent from the average losses in potato IT. High losses of phosphates and ammonium N were observed in a specific event (7 May 2012) during crop cycle 2. This date fell two weeks after the second potato fertilization. Overall, nutrient losses in sediments were much lower than the magnitude of nutrient losses estimated in runoff water (Figures 4 4 4 6). Inceptisols Surface runoff volume and sediments As in site 1, the type of crop cultivated in site 2 has a significant effect on levels of runoff from ind ividual sampling events and from the whole crop cycle during some crop cycles, but for other crop cycles crop type ha d no bearing on these effects (Table 4 7). Similarly to site 1 during crop cycle 1, runoff water volume per sampling event was higher in ry egrass plots than in plots with other covers. Also, the sum of total water losses by runoff in each crop cycle differed by crop type in crop cycles 1 and 3; hence, a significant crop by cycle interaction was found (p = 0.016). Runoff water was higher duri ng crop cycle 1, followed by crop cycle 2; crop cycle 3 had the lowest runoff water levels. During these cycles, the highest levels of runoff were measured in ryegrass plots. Although in crop cycle 2 there were not differences across covers, ryegrass tende d to also have larger amounts of total runoff. The main difference with site 1 was the high runoff volume produced in oats and the lack of differences between runoff in potato CT and potato IT (when differences across covers were found). In site 1 run off f or oats was the lowest vis Ã vis other crops, while in site 2, during crop cycle 1 and 3, runoff for oat crops exceeded the levels measured in potato
98 CT and potato IT. Also, runoff volumes in potato CT were similar to those obtained in potato IT during a ll cycles. As in site 1, higher water losses resulting from runoff in ryegrass occurred during specific events in crop cycles 1 and 2 (sample event x crop interaction, p < 0.05 ; Table 4 3). The magnitude of soil losses was smaller than those reported in si te 1 and those losses were different across covers only during crop cycle 1. Potato IT presented the highest values (as in site 1) and although these tended to be higher than the losses observed in potato CT, there wer e no statistical differences between l osses measured in these two treatments (Table 4 8). High soil losses in potato IT were registered, especially in one event (cover x sample interaction, p = 0.001, Table 4 3). Concentration of nitrate N, ammonium N and phosphorus in runoff water and eroded soil The means of nitrate N concentration in runoff water were different across covers during all crop cycles; potato IT cover had the highest concentrations of nutrients measured in runoff water. During crop cycle 1, potato CT also presented high concentr ations, levels significantly similar to those found in potato IT. Conversely, concentrations in crop cycles 2 and 3 of potato CT were significantly lower from those registered for potato IT (Table 4 9). With respect to phosphate and ammonium N concentrati ons in runoff water, these were higher and significantly different in potato IT than in other crops during crop cycle 3. Overall , all nutrient concentrations tended to be higher in crop cycle 3, as similarly observed in site 1. High concentrations of ammon ium N, nitrate N and phosphate during crop cycle 3 were reported in specific sampling events (cover x
99 sampling event interactions significant at p<0.05), including sampling events that as in site 1, followed fertilization applications (Table 4 3). The cove r did not have a significant effect on ammonium N and phosphate concentrations in sediments (Table 4 10). Rather, nitrate N concentration was comparatively higher in potato IT during crop cycle 1, followed by concentrations in sediments produced in potato CT and oats. The concentrations of nitrate N in potato IT, potato CT and oats were not statistically different. Total nutrient losses in runoff water and eroded soil Total nutrient losses were significantly different across crops in crop cycle 3, where p otato IT presented the highest losses of nitrate N, ammonium N and phosphates in runoff water and these losses were significantly different from those estimated for the other covers. In crop cycle 2, there were only differences in the total losses on nitra te N from different covers, with potato IT presenting the highest losses. In crop cycle 1, there were not differences across covers (Figure 4 7). Total nitrate N losses in sediments were significantly higher in potato IT during crop cycle 1; and similarly across c rop s for the other nutrients (ammonium N and P) (Table 4 8). It is worth noting that total nutrient losses in sediments were much lower than the magnitude of nutrient losses estimated in runoff water (Figures 4 9 4 11). Sampling event effect As m entioned above, major soil and water losses due to water erosion are concentrated in specific moments within the study period (Table 4 3). In site 1, most soil losses occurred in one week of crop cycle 2 and major runoff water was produced in two weeks, on e during crop cycle 2 and one during crop cycle 3. In site 2, most of soil losses occurred in one week of crop cycle 2; while major runoff water losses occurred in
100 three sampling events (one during crop cycle 1 and two during crop cycle 2). The highest tot al losses of nutrients in sediments occurred in these events and were consistently higher in potato IT followed by potato CT. Major water borne nutrient losses occurred as a result of runoff events , typically in which oats and ryegrass presented high valu es. However, when these events followed first or second round fertilization applications, the nutrient losses (soluble P and N) were also high in potato plots. Discussion Impacts of crops in nutrient concentration and losses in runoff water and sediments This study was implemented to evaluate differences between crops with respect to nutrient concentration in r un off water and sediments as well as total nutrient, runoff water and sediment losses. Also, with the aggregated impact of the different crops in ea ch respective rotation, the study intended to identify any trend on the way different rotations, with and without CT, impact these variables. In relation to the impact of the different crops in a particular rotation, the results showed statistical differe nces across crops in some, but not all, crop cycles, in terms of: sediment production; runoff water; and nutrient concentration and losses. When these differences were found, the trend was that potato IT presented the highest values, except for runoff, whi ch was higher in ryegrass in both sites. Also, as will be discussed later, values obtained in potato IT were generally similar to those obtained in potato CT, and were only statistically different in some crop cycles in site 2 (Inceptisols). The main simi larities in the results obtained in the two sites were: i) soil losses did not differ statistically between potato IT and potato CT when differences across
1 01 covers were found; however, these losses tended to be higher in potato IT in Andosols; ii) nutrient concentrations in runoff water (an in sediments for site 1) were higher in crop cycle 3; iii) major runoff and soil losses were associated with specific events and when these coincided with fertilization applications, major nutrient losses were observed; iv) as mentioned above, runoff was higher in ryegrass in both sites; and v) higher concentrations of P and ammonium N were observed in sediments than in runoff water; while higher concentrations of nitrate N were found in runoff water (or were similar to t hose measured in sediments). Instances where soil losses relating to water erosion in potato plots consistently occurred, irrespective of the tillage method employed (i.e. conservation or intensive) indicate that remaining residues from precedent cover cr op in potato CT does not limit soil loss relative to potato IT, especially during those specific events when highest loss typically occurs. This phenomenon might be caused by the residues already in advanced decomposition stages, when the soil erosion occu rs. These events were observed in site 1 and 2 some 70 and 100 days , respectively , after potato sowing and earthing up was conducted (the latter refers to when mounds of soil are drawn up around the plant and, as a result, any remaining residue is mixed wi th the soil). Figure 4 20 , shows the high soil cover provided by oats at the moment of potato sowing and after 30 days. Also, considering the slopes of these areas and the amounts of precipitation, it is unlikely that erosion can be minimized significantly merely with the use of cover crop and reduced tillage in potato cropping. This view differs from reports from other studies that have indicated that CT can achieve reductions in sediment production and this offset increases in nutrient concentration (e.g. McDowell and Mc.Gregor, 1985;
102 Barisas et al. , 1978; Bertol et al., 2003). However, the divergence of these findings, from previous studies, is related to the management of residues in tubers crops vs. other crops (e.g. cereals). Carter (1994) explained th at CT and the management of crop residues must be combined with other technologies (such as mulching), to ensure adequate soil cover throughout potato cropping. The concentration of runoff, sediments and nutrient losses in specific events is an aspect alr eady highlighted in other studies. For example, Thomas et al . (1992) reported peak nitrogen and phosphorous losses during conditions of maximum runoff and erosion. Also, similarly to the findings of this study, higher nutrients in the runoff when fertilize r application coincides with rain was previously noted by Cassol et al. (2002) as well as reported in a review about phosphorus losses in runoff conducted by Hart et al. (2004). With respect to the occurrence of high nutrient concentrations in crop cycle 3 of both sites, this is explained by differences in precipitation; more precipitation occurred during the first two cycles, while the third cycle was unusually drier (also reflected in the lower runoff for this period). During drier conditions, it is poss ible that nutrients accumulated on the soil surface due to the lack of mobilization of nutrients throughout the soil profile (as they would have had they been solubilized in water). Consequently, when a runoff event occurs, the accumulated nutrients in the surface were washed out resulting in runoff water with very high concentrations of these nutrients. This finding is corroborated by the concentration of nutrient losses in runoff in specific events, as described in the previous section.
103 The higher runoff volume in ryegrass during crop cycles 1 and 2 are most likely due to soil compaction and low infiltration of those plots (which had not been tilled and had been grazed prior to the establishment of the experiment in site 1 and 2 for some 3 and 5 years , re spectively. Ryegrass in crop cycle 2 was in the same management condition as in crop cycle 1; therefore, relative high runoff in comparison to other covers was expected. Despite differences in runoff across crops, it is worth highlighting that in the two s tudy sites, there was a low percentage of rainfall that becomes runoff. This reflects the high capacity of these soils, specially of Andosols in the Andes to store water (2 g g 1 ), relating to its characteristic: very high porosity; high organic matter con tent; open and porous structure; rapid hydraulic conductivity; and presence of amorphous clay minerals, such as allophane and imogolite (Buytaert et al., 2005, 2006). In relation to higher P concentrations in sediments, this is caused by its strong colloid soil adsorption, and consequently, its low solubility in runoff (Barbosa et al. , 2009). However, this does not mean that most of the P was lost in the sediments. Higher runoff volume resulted in higher losses of P in water than in soils in most of the cas es and the losses in sediments were similar to the losses in runoff only when high sediment losses occurred. This same compensatory effect has been reported in Barbosa et al. (2009). Nonetheless, it is worth noting that P concentrations in sediments corres pond only to inorganic P as this is the P type extracted by the method used in the lab . This means that P concentrations in sediments may be higher as organic P or even all inorganic P was not extracted in the lab. While a similar compensatory effect (as described above for P losses) happened with ammonium N, the opposite occurred with nitrate N, where losses and
104 concentrations were higher in runoff water than in sediments . Similar results have been reported in studies about nutrient transfer in runoff, wh ere the lost amount of nitrate N was directly affected by the amount of runoff volume (Borin et al., 2005; Ceretta et al., 2010). One of the most significant differences between the findings of the two sites was the differing effect intensive vs. conservat ion tillage for potato crops had on nutrient losses and concentrations in runoff and sediments. In Andosols (site 1), when differences across crops were found, potato CT and potato IT presented higher and similar values of nutrient losses and concentration s except for runoff and total ammonium N losses in runoff water (that were significantly lower in potato CT than in potato IT). Reduced runoff in potato CT, compared to potato IT, in this type of soil, might be a result of the cover crop previously sown i n those plots. In this case, oats utilized as a cover crop might have increased the organic matter, soil aggregation and water storage. This is an effect of CT already reported in a previous study in the same system (e.g. Quintero, 2009; Quintero and Comer ford, 2013). However, to verify the consistency of this impact over time, longer term results from this experiment are required. If the inclusion of cover crops into potato rotation is found to consistently reduce runoff in potato plots, this would mean th at cover crop residues can limit runoff from potato plots in Andosols. This result would be contrary to findings of other studies, where residues of cover crop did not limit runoff when compared to other CT systems without crop residues (e.g. Hansen et al. , 2000). This reduction in total runoff in potato CT was enough to compensate ammonium N concentrations in runoff water, resulting in lower total losses of this nutrient in potato CT in comparison to potato IT.
105 For the other variables measured, there wer e no statistical differences between potato CT and potato IT. For example, nitrate N and phosphate concentrations in water produced in potato CT were not compensated by the reductions in runoff. This was due to the still high concentrations of this nutrien t in potato CT, similar to the levels found in potato IT; therefore, total nitrate N losses were similar, statistically, with those from potato IT. The exception to this was in crop cycle 3, when N losses (as ammonium and nitrate ) in runoff water were high er in oats and ryegrass. This finding is discussed later in this section. The lack of differences in nutrient loss between potato CT and potato IT in Andosols indicates the limited effect of differently partitioning the fertilizer into the two applications in potato CT, relative to potato IT. Also, this may imply that the cover crop did not have an effect on either increasing or reducing nutrient loss. This is the result of no effect of cover crop on sediment production in potato CT and of no differences in nutrient concentration in runoff and sediments from plots with potato CT and potato IT. It is likely that, given the cold and humid conditions where these soils are located, the organic matter incorporated by cover crop does not mineralized rapidly enough and then, nutrients from cover crops do not become available in the short term in the soil. In this sense, the amount of available nutrients in the soil in potato CT and potato IT when runoff occurs is determined by the chemical fertilizer applied in simi lar total amounts in both treatments. In the same sense, when environmental conditions become drier (as per crop cycle 3), the decomposition of organic matter incorporated by cover crops results in the accumulation of higher mineral nutrients in the soil, which are lost at high
106 concentr ation levels in runoff as shown by the high values of nitrate N and ammonium N losses in runoff water in oats and ryegrass plots in crop cycle 3. With respect to phosphorus, it is known that, once applied as chemical fertiliz er in Andosols, it is mostly absorbed and fixed with Al and Fe components becoming unavailable to plants (Kimble et al. , 2000). This leads to very high rates of phosphorus application in potato systems in this region of Colombia. It would be thought that, by including a cover crop in the cropping system, the P use efficiency might increase in the subsequent potato cropping (Horst et al., 2001) due to the stimulation of biological P cycling through addition of cover crop residues (Takeda et al. , 2009) and th at the lower amounts of P might be lost in runoff water. However, results here suggest that in high organic m atter soils, cover crops may not be stimulating such P cycling and therefore the uptake of abundant P applied as chemical fertilizer is not enhance d in potato (as a consequence of the incorporation of a cover crop). This is aligned with the statistically similar P concentrations found in runoff water of oats, potato CT and potato IT. In contrast, when nutrient losses were different across crops in t he Inceptisols (site 2) the highest values were found in potato IT and differed from those observed in potato CT. Thus, nutrient losses were affected more prominently by the distinct tillage practices implemented in potato in site 2 than those utilized in site 1. The differences in nutrient loss were due mainly to significant higher nutrient concentrations in runoff water and sediments from potato IT than potato CT (such differences were not observed in site 1) and were not therefore due to differences in r unoff water volume and soil losses (which were not different between potato CT and potato IT in site 2).
107 The observed differences in nutrient losses in the Inceptisols between potato plots with and without CT could be attributed to: i) the effect of parti tioning the fertilizer differently in CT, relative to IT, within the two fertilization applications; or ii) changes in the fate of the nutrients once applied to the soil in plots, with and without CT. Considering that major nutrient loss occurred during sp ecific events that were close to, and shortly after the date of the second fertilization (where the rate of fertilization was higher in CT than in IT), it is not likely then that this is an effect caused by the different partitioning of the fertilizer (oth erwise nutrient losses and concentrations would be higher in potato CT). In this sense, lower losses of nutrients in runoff water produced in potato CT plots may be associated with: a major sorption of these nutrients by soil, avoiding major losses by run off; or to a greater nutrient uptake by potato plants. A probable higher sorption capacity of nutrients in soils with CT may be ultimately the result of increasing organic matter via the incorporation of cover crops before potato cropping. This is reflecte d for example in the lower soluble P losses in potato CT than in potato IT, resulting from either: an improvement in the capacity of soils to sorb P; or by the increase in P uptake by crops obtained by a better P cycling through oats residues ( s ee lower so luble P concentrations in plots cultivated with oats and subsequently in potato CT compared to potato IT, Table 3 9). In contrast, previous studies have shown that the concentration and loss of nutrients for rainfall induced runoff from CT systems is highe r than from conventional tillage systems (Mc Dowell and McGregor, 1984; Romkens et al., 1973; Hansen et al. , 2000). However, in some of these studies CT was strictly no tillage where crop residues and applied nutrients stayed in the soil surface causing lo w
108 mobilization of nutrients into the soil and consequently higher concentrations of nutrients in runoff (McDowell and McGregor, 1984). Differences in tillage practices (reduced vs. no till) may ultimately explain these differences regarding the concentrati on of nutrients in runoff. Another difference between results obtained in Andosols and Inceptisols is that even if soil losses did not differ between potato IT and potato CT in the two study sites, soil losses were higher in site 1 than in site 2, as the nutrient losses transferred by soil erosion. Although, total nutrient losses in sediments tended to be lower than those transferred by runoff water (Figures 4 4 4 6 and 4 9 4 11) in the two sites (except for P losses in site 1 where some higher values were reported in sediments); losses in sediments were more important in the site 1, especially in specific events of high soil losses during which most of the ammonium N and phosphate were lost. Also, higher concentrations of ammonium N and nitrate N in ru noff water, and soil eroded in plots located in this type of soil than in those installed in site 2, reflect the intrinsically higher natural level of nitrogen in Andosols. Thus, the results of this study do not agree completely with results fr om many stu dies that have shown that higher nutrient concentration in runoff and sediments are attained with CT, while runoff and sediments are reduced. Instead, this study shows that CT in potato grown in steep areas in the Andes reduces or maintains nutrient concen trations in comparison with intensive tillage; and only in Andosols does this reduce the runoff. Whether these CT practices reduce or maintain nutrient concentrations and runoff losses vis Ã vis IT, seems to depend upon the type of soil and precipitation c onditions. Although uncommon, similar results have been reported in
109 other studies. Logan et al. (1994) found absence of tillage effects on runoff; this was attributed to the low hydraulic conductivity of the heavy soils where the research was conducted. Th is finding is in contrast with experiences using more permeable soils in which no tillage has significantly reduced runoff (Logan et al., 1991). This is very similar to the differences in reductions of runoff obtained in the Inceptisols and Andosols (Incep tisols being the heavier soils) and included in this experiment. Results from Logan et al. (1991, 199 4), together with results shown in this section , suggest that effects of CT on runoff and nutrient losses cannot be generalized and depend on the type of s oil; as well as the fertilizer application rate and time , as already mentioned. In summary, nutrient and sediment losses are generally lower in Inceptisols than in Andosols and CT seems to have a positive effect on reducing nutrient losses in Inceptisols . In Andosols the impact of CT is not significant, even though in some cases potato IT tended to cause higher nutrient losses than potato CT. In order to reduce nutrient losses, especially in Andosols, adjustment of fertilization rates should be tested in order to reduce the concentration of nutrients in runoff and sediments and their respective loss as a result of runoff. According to these findings, beyond a certain point, the reduction of nutrient loss is not achieved via reducing soil loss. That is, inc reasing crop residues, beyond what already have been achieved by the CT practices promoted in this region, may not be enough to reduce nutrient losses. An effective reduction of nutrient losses, as phosphorus for example, will require combining practices t o reduce runoff and erosion together with adjustments of nutrient application rates in order to avoid the excessive buildup of phosphorous in the soil, beyond the crop requirements (Sharpley et al., 2001).
110 Impact of the whole rotation on nutrient and sed iment losses Up to this point, the impacts of diff erent crops in the rotations on runoff a nd nutrient and sediment losses have been discussed. However, the question remains : what the impact of the whole rotation/system is in terms of nutrient a n d soil loss es? To respond to this question it is necessary to consider the results in a rotation based perspective, as there is evidence that the best practice to avoid erosion is the integration of crop pasture rotations with CT practices (Ernst and Siri Prieto, 200 7). In this sense, it is worth analyzing both the performance of the rotations as well as the aggregated impacts of the different crops in the rotation (with and without CT). Figures 4 12 and 4 13 show the averaged total nutrient losses per type of rotati on over three crop cycles. As expected, the combined effect of the different crops in nutrient and sediment losses depends upon: the frequency of a crop within the rotation; the behavior of the environment in each given crop cycle; and the soil type. In An dosols, results demonstrate intensive tillage rotations (potato IT potato IT ryegrass) as presenting the highest N losses (as ammonium N and nitrate N). In the case of phosphorus the total losses tend to be similar across the systems. It is worth notin g that, although is an intensive tillage system, IT2 (pasture pasture potato) presented lower values of total nutrient losses, results that are explained by the lower losses observed in ryegrass (despite its high runoff rates). In Inceptisols, IT2 (ryegras s ryegrass potato IT) tend to have the highest P and ammonium N losses; a pattern that is explained by the high losses in potato IT in crop cycle 3. However, nitrate N losses were more similar across systems and CT2 (which comprised two oats cropping s easons in the rotation) tended to have smaller nitrate N loss. That is, these results do not indicate the same pattern across the two sites; the impact of conservation tillage seems to be very specific to each
111 s respective environment (as observed in the difference between crop cycles). Consequently, these rotations need to be observed in the longer term, so as to compare rotations and thereby evaluate likely impacts of cover crops and conservation tillage pract ices over time and under diverse precipitation magnitudes on nutrient losses. With respect to total soil loss, the average of the sum of total soil losses per rotation (Figure 4 14) shows a trend of higher soil losses in IT1 in the two sites than in the o ther rotations . Although soil losses per crop were not significantly different, potato IT tended to have the higher values and this explains high accumulated total losses during the time of this study. Environmental implications Despite the lack of differ ences in the nutrient concentration in runoff water across crops in various crop cycles, the measured concentrations of nutrients are still very relevant from an environmental perspective. The concentration of nut rients in run off, even if irrelevant from a n agronomic perspective, can have a deleterious impact on water quality (Alberts and Spomer, 1985; Barbosa et al. , 2009). Phosphorus and nitrate can pollute water causing the eutrophication of water bodies (Sharpley et al., 1987; Pote et al. , 1996) or even becoming a threat to human health (Rubiano et al. , 2006). Phosphorus, although not very soluble in water, becomes bio available in water causing eutrophication (McIsaac et al. , 1995). In this sense, it is important to reduce losses of nitrate and phosphat es in water erosion in agricultural landscapes, since the level of these nutrients in surface runoff from cultivated lands can easily exceed the threshold beyond which they can cause a negative environmental impact. The concentrations of phosphorous and ni trate determined in this work were above the limit recommended despite of the lack of differences in the nutrient concentration in runoff water between
112 crops in some crop cycles. The obtained nitrate N concentrations in site 1 and site 2 exceeded the maxi mum value recommended of 10 mg/L for surface and groundwater by EPA (2014) and ICONTEC (1994), during crop cycle 3 in potato IT, oats and ryegrass. Also, phosphorus concentration s exceeded the limit of 0.1 mg/L proposed by WHO (2004) in all crops and crop cycles, and were notably higher in potato cultivated plots (Figures 4 16 and 4 19). For ammonium N, the OMS has a limit of 1.5 mg/ L in water for human consumption, which was also exceeded in all crops and cycles. The phosphorus limit can be relatively cons ervative considering that other authors have indicated lower limits for this nutrient. Vollenweider (1971) identified a level of 0.01 mg/L as already prejudicial for water bodies as it can trigger eutrophication processes. Considering these more strict per missible leve l s, the phosphorus concentrations obtained in the two sites of this study can be even more dramatic and a major concern considering that the Fuquene Lake, located downstream of the study site, is facing an accelerated eutrophication process. S ignificant lower concentrations of ammonium N; nitrate N and P in potato CT in comparison to potato IT (especially in Inceptisols during crop cycles 2 and 3) are very relevant as, from an environmental perspective, these indicate a benefit that conservatio n tillage can provide, even if total nutrient losses are not significantly different or if its magnitude is not relevant for crop farmers. Summary Higher soil, water and nutrient losses (caused by runoff) occurred during specific events; nutrient losses we re at their highest when such events followed the applications of fertilization. The reduction of these losses from agricultural areas has been the expected impact of promoting conservation tillage in the Fuquene watershed. However, nutrient and soil losse s have not always been reduced significantly with conservation
113 tillage (at least during the period of study of this research). Indeed, the impact of conservation tillage was positive in the Inceptisols site, where nutrient losses were significantly lower i n potato with conservation tillage than with intensive tillage. This was due to a reduction in nutrient concentration primarily and not by a reduction in runoff caused water and soil losses. However, this was not the case in the research site with Andosols where, although reductions in runoff were attained with CT in potato cultivation, nitrogen (ammonium N and nitrate N) and P losses did not differ significantly between these tillage treatments. In Andosols it is likely that the combination of high fertili zers application; intrinsic high nutrient content in soils; and additional input of organic matter through the incorporation of cover crops is causing an excess of nutrients in the system and its subsequent loss in runoff caused water and sediments. Rega rdless of differences among crops regarding nutrient losses by water erosion, concentrations of phosphorous found in all plots (and of nitrate N in potato IT and oats plots) are above the environmental permissible limits. Hence, further adjustments to fert ilization rates (beyond the different partition done in this experiment) should be tested to evaluate whether lower fertilization rates in conservation tillage might reduce nutrient concentrations, especially during unavoidable punctual soil losses events. In respect to the impact of whole rotations, those with IT (i.e. sites 1 and 2) showed higher nitrate N and sediment loss values. Nonetheless, it is recommended that these rotations continued to be measured in the longer term, in order to capture the like ly impacts of conservation tillage over time in the whole rotation system and so as to confirm the trends found in this study.
114 Figure 4 1. Total rainfall and average runoff per cover treatment and crop cycle . A) site 1; B) site 2. 0 50 100 150 200 250 300 350 400 450 500 1 2 3 Rainfall or runoff (mm) Crop cycle Potato-CT Potato-IT Oats Ryegrass Precipitation 0 100 200 300 400 500 600 1 2 3 Rainfall or runoff (mm) Crop cycle Potato-CT Potato-IT Oats Ryegrass Rainfall A B
115 Tabla 4 1. Crop sequence and planting dates per type of rotation Rotation and planting/harvesting dates Crop cycle 1 Crop cycle 2 Crop cycle 3 Rotation CT1 Cover Potato CT Oats Potato CT Planting date 9 12 August, 2011 3 March, 2012 4 6 October, 2012 Harvesting date 22 23 January, 2012 1 August, 2012 21 22 January, 2012 Rotation CT2 Cover Oats Potato CT Oats Planting date 9 August, 2011 2 3 March, 2012 4 October, 2012 Harvesting date 13 January, 2012 14 15 August, 2012 10 March, 2013 Rotation IT1 Cover Pot ato IT Potato IT Ryegrass Planting date 9 12 August, 2011 2 3 March, 2012 Harvesting date 22 23 January, 2012 14 15 August, 2012 Rotation IT2 Cover Ryegrass Ryegrass Potato IT n.a. n.a. 4 6 October, 2012 21 22 January, 2012 s date is when oats residues were cut and left on top of soil surface n.a. not applicable Table 4 2. Surface runoff volume per cover and crop cycle in study site 1 Land cover Runoff (L /10 m 2 ) ANOVA: cover; P value Potato CT Potato IT Oats Ryegrass Runoff at each measurement event Crop cycle 1 34.57 (ab) 21.78 (a) 12.43 (a) 50.99 (b) 0.042* Crop cycle 2 6.51 (a) 15.63 (b) 6.24 (a) 10.78(ab) 0.016* Crop cycle 3 19.47 15.89 18.47 19.29 0.886 To tal runoff per crop cycle Crop cycle 1 207.39 130.68 74.59 305.10 0 .274 Crop cycle 2 65.08 (a) 156.31 (b) 62.43 ( a ) 107.83(a b ) 0.029* Crop cycle 3 77.90 62.35 73.87 77.18 0.787 Potato with conservation tillage (Potat o CT); Potato with intensive tillage (Potato IT) Means followed by the same letter (in parenthesis) are not significantly different * Significant at 5%;** Significant at 10%
116 Table 4 3. Analysis of variance of runoff, soil and nutrient losses per sampling event Site 1 (Andosols) Site 2 (Inceptisols) ANOVA: sample event effect Week with higher Cover with higher values ANOV A: sample event effect Week with higher values Cover with higher values Runoff (l) 0.000* Ryegrass 0.000* 1,3,5,22 ,23 Ryegrass Soil losses (kg) 0.004* Potato IT Potato CT 0.001* 1 Potato IT Potato CT Nutrient loss in runoff NH4 N 0.000* 49 Oats 0.004* Ryegrass NO3 N 0.010* 49 Oats 0.000* Potato IT Ryegrass PO4 0.000* Pot ato IT Potato CT 0.000* Ryegrass Potato IT Potato CT Nutrient loss in sediments NH4 N 0.000* Potato IT Potato CT 0.136 NO3 N 0.188 0.001* 1 Potato IT PO4 0.000* Potato IT Potato CT 0.004* 1 Potato CT Potato IT Weeks with higher values and statistically different from values of other weeks at 95% of significance One week after first or second fertilization (first fertilization in crop cycle 1 was on April 27 th and measurement on week 25 th was on Jun e 8 th , 2011; first fertilization in crop cycle 3 was on October 5 th and measurement on week 48 th was on October 15 th ; second fertilization in crop cycle 3 was on November 3 rd and measurement on week 56 th was on November 11 th , 2012) . * Significant at 5%
117 Table 4 4. Soil losses per cover and crop cycle in study site 1 Land cover Soil losses (k g/10 m 2 )) ANOVA; P value Potato CT Potato IT Oats Ryegrass Soil losses per sample Crop cycle 1 0.07 0.06 0.03 0.02 0.215 Cr op cycle 2 0.75(ab) 1.78(b) 0.24 (a) 0.06(a) 0.100** Crop cycle 3 0.02 0.12 0.02 0.08 0.207 Total soil losses Crop cycle 1 0.37 0.30 0.13 0.11 0.406 Crop cycle 2 0.75(ab) 1.78(b) 0.24(ab) 0.06(a) 0.100** Crop cycle 3 0.03 0.16 0.03 0.11 0.258 Potato with conservation tillage (Potato CT); Potato with intensive tillage (Potato IT) Means followed by the same letter are not significantly different * Significant at 5%;** Significant at 10% Table 4 5. Concentration of nutrients in runoff water in site 1 Land cover Potato CT Potato IT Oats Ryegrass ANOVA; P value NH 4 N (mg/ L ) Crop cycle 1 3.86 3.44 1.90 1.86 0.523 Crop c ycle 2 4.07 3.51 1.90 1.68 0.131 Crop cycle 3 3.87 13.81 11.12 9.63 0.427 NO 3 N (mg/L ) Crop cycle 1 3.20 (b) 2.62 (b) 0.28 (a) 0.16 (a) 0.002* Crop cycle 2 3.59 (b) 4.94 (b) 1 .10 (a) 1.15 (a) 0.001* Crop cycle 3 2.90 6.06 17.54 16.41 0 .51 0 PO 4 (mg/L ) Crop cycle 1 0.41 0.51 0.41 0.64 0.91 0 Crop cycle 2 0.34 (a) 0.56 (ab) 0.48 (ab) 0.85 (b) 0.07 0 ** Crop cycle 3 1.23 2.06 0.33 0.33 0.14 0 Potato with conservation tillage (Potato CT); Potato with intensive tillage (Potato IT) Means followed by the same letter are not significantly different * Significant at 5%;** Significant at 10%
118 Table 4 6. Concentration of nutrients in sediments in site 1 Land cover Potato CT Potato IT Oats Ryegrass ANOVA; P value NH4 (mg/kg) Crop cycle 1 68.17 74.21 164.05 148.71 0.61 0 Crop cycle 2 790.71 413.74 372.96 229.54 0.457 Crop cycle 3 28.34 63.54 30.83 93.24 0.418 NO3 (mg/kg) Crop cycle 1 1.66 (ab) 4.99 (b) 0.45 (a) 0.99 (a) 0.039* Crop cycle 2 69.55 58.23 0.26 0.28 0.217 Crop cycle 3 0.40 39.59 0.19 14.33 0.161 P (mg/kg) Crop cycle 1 42.50 20.71 17.27 16.63 0.548 Crop cycle 2 157.96 87.69 69.37 70.98 0.842 Crop cycle 3 64.46 76.19 4.37 72.71 0.548 Potato with conservation tillage (Potato CT); Potato with intensive tillage (Potato IT) Means followed by the s ame letter are not significantly different * Significant at 5%;** Significant at 10%
119 Figure 4 2. Total nutrient losses in runoff water in site 1 (Andosols). A) NH 4 N; b) NO 3 N and C) P. ( Note: Means followed by the same letter are not signifi cantly different; NS: Not significantly different) A B C
120 Figure 4 3. Total nutrient losses in sediments in site 1 (Andosols). ). A) NH 4 N; b) NO 3 N and C) P. ( Note: Means followed by the same letter are not significantly different; NS: Not signif icantly different) B A C
121 Figure 4 4. Total NH 4 N losses in sediments and runoff water in site 1 (Andosols).
122 Figure 4 5. Total NO 3 N losses in sediments and runoff water in site 1 (Andosols).
123 F igure 4 6. Total P losses in sediments and runoff water in site 1 (Andosols).
124 Table 4 7. Surface runoff volume per cover and crop cycle in study site 2 Land cover Runoff (L /10 m 2 ) ANOVA:cover; P value Potato CT Potato IT Oats Ryegrass Runoff at each measurement event Crop cycle 1 38.45 (ab 22.62 (a) 71.39 (bc) 90.89 (c) 0.009* Crop cycle 2 20.65 18.50 14.84 29.74 0.613 Crop cycle 3 11.00 12.62 13.80 16.67 0.303 Total runoff per crop cycle Crop cycle 1 230.68 (ab) 135.72 (a) 428.35 (b) 545.32 (c) 0.012 * Crop cycle 2 186.15 166.49 133.57 267.62 0.538 Crop cycle 3 55.01 (a) 63.09 (ab) 68.98 (ab) 83.37 (b) 0.157* Potato with conservation tillage (Potato CT); Potato with intensive tillage (Potato IT) Means followed by the same letter (in pa renthesis) are not significantly different * Significant at 5%;** Significant at 10% Table 4 8. Soil losses per cover and crop cycle in study site 2 Land cover Soil losses (k g/10 m 2 )) ANOVA; P value Potato CT Potato IT Oats Ryegrass Soil losses per sample Crop cycle 1 0.06(ab) 0.13(b) 0.02(a) 0.01(a) 0.029* Crop cycle 2 ----------Crop cycle 3 ----------Total soil losses Crop cycle 1 0.19 (ab) 0.39 (b) 0.06 (a) 0.08 (a) 0.071** Crop cycle 2 ---------Crop cycle 3 ----------Potato with conservation tillage (Potato CT); Potato with intensive tillage (Potato IT) Means followed by the same letter are not significantly different * Significant at 5%;** Significant at 10%
125 Ta ble 4 9. Concentration of nutrients in runoff water in site 2 Land cover Potato CT Potato IT Oats Ryegrass ANOVA; P value NH 4 N (mg/L ) Crop cycle 1 1.84 1.76 2.28 2.92 0.580 Crop cycle 2 2.10 2.23 2.72 2.00 0.947 Crop cycle 3 (a) 4.04 (b) 16. 86 (a) 2.73 (a) 2.19 * 0.023 NO 3 N (mg/L ) Crop cycle 1 (b) 2.29 (b) 2.04 (ab) 1.06 (a) 0.14 * 0.018 Crop cycle 2 (a) 0.48 (b) 2.12 (ab) 1.17 (a) 0.89 * 0.017 Crop cycle 3 (a) 3.39 (b) 10.93 (a) 4.12 (a) 3.81 * 0.017 PO 4 (mg/L ) Crop cycle 1 1.43 0.9 2 1.45 1.13 0.384 Crop cycle 2 0.99 2.84 1.53 1.12 0.637 Crop cycle 3 (a) 2.19 (b) 8.05 (a) 1.92 (a) 0.88 0.037 Potato with conservation tillage (Potato CT); Potato with intensive tillage (Potato IT) Means followed by the same letter are not signifi cantly different * Significant at 5%;** Significant at 10% Table 4 10. Concentration of nutrients in sediments in site 2 Land cover Potato CT Potato IT Oats Ryegrass ANOVA; P value NH4 (mg/kg) Crop cycle 1 41.27 34.00 19.37 64.87 0.672 Crop cycle 2 ----------Crop cycle 3 ----------NO3 (mg/kg) Crop cycle 1 (ab) 0.68 (b) 2.36 (ab) 0.71 (a) 0.33 ** 0.083 Crop cycle 2 ----------Crop cycle 3 ----------P (mg/kg) Crop cycle 1 87.11 46.60 40.03 22.63 0.344 Crop cycle 2 ----------Crop cycle 3 ----------Potato with conservation tillage (Potato CT); Potato with intensive tillage (Potato IT) Means followed by the same letter are not significantly different * Significant at 5%;** Signifi cant at 10%
126 Figure 4 7. Total nutrient losses in runoff water in site 2 (Inceptisols). A) NH 4 N; b) NO 3 N and C) P ( Note: Means followed by the same letter are not significantly different; NS: Not significantly different) A B C
127 Figure 4 8. Total nutrient losses in sediments in site 2 (Inceptisols). A) NH 4 N; b) NO 3 N and C) P. ( Note: Means followed by the same letter are not significantly different; NS: Not significantly different) A B C
128 Figure 4 9. Total NH 4 N losses in sediments and ru noff water in site 2 (Inceptisols).
129 Figure 4 10. Total NO 3 N losses in sediments and runoff water in site 2 (Inceptisols).
130 Figure 4 11. Total P losses in sediments and runoff water in site 2 (Inceptisols).
131 I Figure 4 12. Total nu trient losses in sediments and runoff water per rotation type in site 1 (Andosols). A) NH 4 N; B) NO 3 N and; C) P. (Note: CT1: Potato CT Oats Potato CT; CT2: Oats Potato CT Oats; IT1: Potato IT Potato IT Ryegrass; IT2: Ryegrass Ryegrass Pota to IT) A B C
132 Figure 4 13. Total nutrient losses in sediments and runoff water per rotation system in site 2 (Inceptisols). A) NH 4 N; B) NO 3 N and; C) P . (Note: CT1: Potato CT Oats Potato CT; CT2: Oats Potato CT Oats; IT1: Potato IT Potat o IT Ryegrass; IT2: Ryegrass Ryegrass Potato IT) A B C
133 Figure 4 14. Average of the sum of total soil losses per rotation system in : A) site 1 Andosol ) and B) 2 Inceptisols . (Note: CT1: Potato CT Oats Potato CT; CT2: Oats Potato CT Oats; I T1: Potato IT Potato IT Ryegrass; IT2: Ryegrass Ryegrass Potato IT) A B
134 Figure 4 15. Average of the sum of total runo ff water per rotation system in: A) site 1 Andosols and B) site 2 Inceptisols. (Note: CT1: Potato CT Oats Potato CT; CT2 : Oats Potato CT Oats; IT1: Potato IT Potato IT Ryegrass; IT2: Ryegrass Ryegrass Potato IT) A B
135 Figure 4 16. Nutrient concentration in runoff and permissible limits (Andosols). A) Ammonium N and B) nitra te N concentrations A B
136 Figure 4 17. Phosphorous concentrations in runoff and permissible limits according to recommended levels (Andosols).
137 Figure 4 18. Nutrient concentrations in runoff and permissible limits (Inceptisols). A) Ammonium N and B) nitrate N concentrations A B
138 F igure 4 19. Phosphorous concentrations in runoff and permissible limits (Inceptisols).
139 (A) (B) Figure 4 20. Cover cr op residues in potato cropping: A ) at potato planting plots with and without conservation tillage are shown; B ) 30 days afte r planting in conservation tillage plots .
140 CHAPTER 5 CONCLUSIONS The global demand for food is increasing and this trend is projected to continue for decades to come, giving rise to the need for increased crop production (Godfray et al., 2010). The key ways in which increased production can be achieved through approaches such as expanding agricultural areas or by agricultural intensification. Given the recognized need to conserve remaining natural ecosystems and reduce greenhouse gases emissions (bot h associated with land clearing), sustainable intensification is proposed as a pathway to raise the production of food without increasing the environmental impacts associated with land clearing (Tilman et al. 2011). I n this context, mixed crop livestock sy stems combined with soil conservation practices may provide ways to increase revenue and reduce environmental impacts; hence, further research into these systems is required (Franzluebbers, 2007). Approaches for achieving greater crop yields, while lower ing environmental impacts, and hence achieving sustainability in i ntensified agriculture systems require assessment of the socioeconomic context (Garnett and Godfray, 2012); these should be done to ascertain the likelihood of adoption of these approaches. Furthermore, scientific evidence is required not only on yields but also on environmental impacts. Thus, the assessment of sustainable intensification options require a holistic approach, as well as sustained long term research into their impacts on all ob jectives: crop productivity; environmental conservation; and the enhanced socio economic conditions of farmers and their broader communities. A review of previous and current research on the subject of the present study (ie the two sod based rotations (SB R) developed for the Southeastern US and Colombia) provides a clear understanding of which objectives are
141 well studied and which require further consideration. The following is a summary of current knowledge about these SBRs, including the findings of this present study; the last section provides recommendations for areas warranting further research. Sod based rotations with conservation tillage in Southeastern US From the productivity perspective, increases in peanut and cotton yields as a result of utiliz ing sod based rotations (SBR) have been extensively reported in previous studies (Marois and Wright, 2003; Wright et al., 1992; Dickson and Hewlett, 1989; Elkins et al., 1977). These increases are believed to be associated with: the reduced incidence of ne matodes; increased rooting depth (Katsvairo et al. 2007); and the resultant enhances soil properties (Marois and Wright, 2003). From the environmental perspective, the integration of perennial grasses into conventional peanut cotton rotations has been show n to reduce soil erosion and nitrate leaching and simultaneously increase soil carbon storage (Kastvairo et al. 2006a, Wright et al. 2004), compared with conventional peanut cotton r otation. These perennial grass rotations have also measurably decreased t on water and soil quality. Sufficient scientific evidence strongly supp orts the hypothesis that SBR is environmentally sound and enhances productivity (Parr et al., 1990). Also, such systems are thought to work in land already dedicated to agriculture and moreover, because of enhancing land use efficiency, do not require agricultural expansion. In this regard such SBR systems constitute an example of sustainable agriculture. As for many sustainable agricu lture options, the design of SBR systems should be context specific (Pretty, 1997). In this sense, the SBR was designed for small farmers of the Southeastern region in the
142 stem is attractive to 45% of interviewed farmers from the Southeastern US, who indicated that they are willing to adopt it in the short term (this statistic does not include farmers that do not know about the system and might become potential adopters if i are characterized as: owners of low income farming operations (i.e. US$50,000 $99,999 per year); aged 50 years old or under; recipients of extension promoters, through whom they first heard about SBR; managers of conventional peanut cotton rotation using intensive tillage methods and; owners of cattle that are partially if not totally fed with supplements. Thus, although SBR can be practiced in any crop rotation it is worth noting that the outline d system is the sort in which most UF NFREC led SBR related research and extension work is done. From the economic perspective, the profitability analysis conducted in this dissertation also confirms that SBR is more profitable in the long term than the conventional peanut cotton rotations using intensive tillage practices. For instance, for small peanut cotton producers this study showed that SBR can generate important increases in net revenues (in the range of US$278,000 to $490,000 over ten years) rela tive to the conventional peanut cotton rotation that uses conventional tillage practices. Comparatively higher net revenues with SBR are explained by the remarkable enhancement in crop yields (almost double), when cotton and peanuts are rotated with bahiag rass. Higher crop yields resulting from SBR compensate the reduction this system entails in regards to land availability for crops, given that part of the land is required to be allocated bahiagrass cultivation. The net revenue can be further maximized in a SBR if the bahiagrass is used to produce hay. Furthermore,
143 using this system, peanuts can be grown without irrigation; and cost reductions result from decreased need for pest and disease control. Such findings are supported by Katsvairo et al. (2007) as well as by records from the long term experiment at NFREC (as used in this analysis). Also, this system is more profitable for farmers that own non integrated crop and cow calf operations. When conservation tillage practices and perennial grasses are full y integrated into the cotton peanut rotation by farmers who own cattle, the net revenue over 10 years is further increased compared to a farm growing these crops and raising cattle separately. This increase is due to an additional enhancement of yield when peanuts and cotton are grown in land previously used to grow bahiagrass. Additionally, the usual negative revenues from the cow calf operation in the region (when total costs are included in the economic analysis) are reduced due to increases in weight at calving and in stocking rates. Although the net revenue above total costs in cow calf operation remains negative, the total revenue for the whole system is still comparatively much higher using SBR. Increases in crop yields in this crop pasture rotation h ave been further explained by the recycling of nutrients in the system, allowing crops to benefit from the greater residual fertility left by cattle grazing (George et al. 2013). Similar to the results obtained for a crop grower, SBR revenues are maximized for peanuts, as their irrigation needs are removed; likewise, SBR use results in reduced pest and disease control costs. In summary, in farms that grow peanuts and cotton in a conventional man ner and have livestock, SBR has the potential to boost crop yie lds and revenues without requiring any further expansion of the agricultural land. For cotton peanut farmers who
144 do not own cattle and are not interested in performing cow calf operation, the SBR will similarly result in higher economic benefits, especiall y if conservation tillage practices have not previously been incorporated in their respective systems. SBR is also conducive to increases in crop yields and reductions in environmental impacts; the realization of these benefits does not require agricultura l expansion. Sod based rotations with conservation tillage in Colombia This system was first promoted in Colombia in 1999, by the regional environmental authority (CAR) and was designed for small potato farmers in the Andean hillsides, based on the prin ciples of conservation tillage (soil coverage and minimum soil disturbance). Although Colombian farms are typically very small (between 5 10 hectares), it is expected that the adoption of these practices, in aggregate, might produce a significant impact on the quality of the water at the watershed level (Rubiano et al. 2006). Research on the three aforementioned dimensions (i.e. productivity, economics and environmental impact) of this system indicates that the incorporation of conservation tillage pract ices into the conventional potato pasture rotation in the mountainous area of Colombia can deliver economic benefits to farmers. Quintero (2009) estimated that the average seven year cumulative net farmer revenue is increased by 17% with conservation til lage compared to using conventional tillage rotations, such increased revenue is due to decreased requirements of machinery operations, as well to increased potato yields (when using potato yield data, as recorded by the CAR). In terms of productivity, in the experiment described in C hapter 4, potato yield data (data not shown) indicated that in the short term the incorporation of cover crops and reduced tillage in the potato based rotation do not reduce yields;
145 increases might be expected with time, as an effect of the cumulative incorporation of organic matter by the cover crops. In terms of environmental impacts, although over a decade the main reason for prompting these practices was to reduce soil nutrient loss from potato crop lands surrounding the Fu quene Lake (which faces threats of eutrophication), it is only now that a systematic research effort has been put into place to formerly investigate these impacts. Previously , environmental considerations were only studied in relation to the impacts of con servation tillage on the carbon sequestration capacity of this system. Quintero and Comerford (2013) found that the potato pasture rotation with conservation tillage is capable, over a 7 year period, of improving the carbon content by 45 % , relative to soil under the conventional potato pasture rotation (without conservation tillage). This improvement was correlated with the enhancement of soil physical characteristics, which in turn, are associated with soil water movement and storage such as: bulk density; available water content; saturated hydraulic conductivity; and mesoporosity (Quintero, 2009). The current study (throug h the experiments described in C hapter 4) contributes importantly toward better understanding the impact of conservation tillage (in a mixed crop livestock system) on nutrient and soil losses. Results show that the impact cannot be generalized, as it varies depending on soil type and precipitation variation between cropping cycles. In general, conservation tillage reduces nutrient (phosph orous and nitrogen) losses in low orga nic matter and heavier soils (I nceptisols). In contrast, in high organic matter and less dense soils (Andosols), conservation tillage does not significantly reduce the nutrient losses from potato plots. Also, soil loss es are not
146 significantly reduced with conservation tillage, although soil loss in conventional potato tillage tended to be higher than those in conservation tillage. In Andosols it is likely that the cause of excessive nutrients in the system (and their su bsequent loss in water and sediment runoff) is caused by a combination of high levels of fertilizer application , high nutrient content in soils , and additional inputs of organic matter through the incorporation of cover crops. From a water pollution persp ective, concentrations of phosphorous during th e whole rotation phases and of n itrate N in potato (with conventional tillage and cover crops, namely oats, plots) are above the recommended limits. Hence, further adjustments to fertilization rates are needed to reduce nutrient concentrations, especially during unavoidable punctual soil losses events. In respect to the impact of whole rotations, those with conventional tillage (in both types of soils) showed higher nitrate N and sediment loss values. Nonethele ss, it is recommended that these rotations continued to be measured in the longer term, in order to capture the likely impacts of conservation tillage over time in the whole rotation system, and so as to confirm the trends found in this study. In relation to the objectives of enhancing crop yields and reducing environmental impacts in agriculture, this mixed crop livestock system in the Colombian Andes has not been researched as extensively as the system studied in the Southeastern US. Nonetheless, existin g scientific evidence shows its ability to increase, or at least maintain farmer revenues. Existing data on productivity does not lead to conclusive results, however, none of this has shown reductions compared to the conventional systems. From the environm ental perspective, the benefits of carbon sequestration
147 have been proven in Andosols, although the impacts on water quality are not generalizable. In general, results prompt recommendat ions on the rates of fertilizer use that may help to refine this system and complement the positive role of cover crops and reduced tillage on: soil carbon content; economic performance; and productivity in the longer term. Furt her research needs Identifying differences between rates of advancement of scientific evidence for the two mixed crop livestock systems (in terms of productivity, economics and environmental impacts) is useful to envision future directions based on this Farmers performing well in non integrated systems are generally not willing to adopt new practices and therefore require some kind of incentive to adopt perennial grasses into crop rotations (with the purpose of reducing negative environmental impacts). There are national programs that recognize these overall environmental benefi subsidies represent a small portion of the total subsidies allocated to farmers and have been distributed well below their authorized levels for the past three years (EWG, 2014), t hey (and/or other incentive based mechanisms) still constitute vital ways to incentivize farmers to undertake sod based rotations and thereby produce economic and environmental benefits for the region. Hence, it is important to provide policy makers with science based evidence about thes e benefits for their incentive designs and allocations. One example of this is the recently reauthorized Environmental Quality Incentives Program that aims to assist growers in the implementation of conservational managemen t practices, including the establishment of bahiagrass.
148 In terms of extension efforts, there is both opportunity and need in the Southeastern US region with farmers willing to adopt the sod based rotation system. Nonetheless, this willingness will only t urn into adoption if direct technical support (as the main means by which farmers receive infor mation on this technology, see C hapter 2) is offered directly in visits to farms. The support to the adoption process can be seen as a gradual process, initiatin g with the mere incorporation of perennial grasses into farms with land yielding below expectations (22% of non potential adop ters are willing to do so; see C hapter 2). Also, extension activities should focus on highlighting the benefits of sod based rotat ions on crop yields and pest reduction, as recognition of these benefits appears to be the main motivation of farmers willing to adopt such a system. Also, the benefits of better nutrient cycles (achieved by the mix of crops with livestock and translated i nto higher stocking rates and weight gains) should be highlighted in extension information provided to farmers with crops and cattle (the category most willing to adopt SBR). From a research perspective, future efforts should be oriented toward providing s peanuts cotton rotation (which has been the main form used over the last decade to g the magnitude of farmers in the region that have, as yet, not received information on the SBR but that might be interested in improving their production systems with the incorporation of conservation tillage practices and perennial grasses into crop rota tions. Finally, results from this study can serve as a baseline upon which future data (collected
149 over time regarding the status of adoption) might provide insights about realized . e. whether they might be classed as early adopters, late adopters or non adopters). From the environmental perspective, further studies on the off site impacts of the sod based rotation on water quality and quantity at the landscape or watershed level; o r on the net impact on greenhouse gas emissions, would also be valuable. Such ecosystem services of regional and global pertinence. In the sod based rotations studied in C olombia, the expansion of the field th respect to nutrient and soil losses) and definition of some environmental benefits gained in the long term . Additionally, it is highly reco mmended that reduced rates of fertilization be evaluated in order to achieve significant reductions in nutrient loss in conservation tillage treatments. From the productivity perspective, although not the primary objective of this research, it would be wor th undertaking long term measurements of yield, attained after various cycles of cover crop and reduced tillage implementation to identify eventual yield increases. With respect to the level of adoption, although there are farmers that have adopted this sy stem, it is recommended that an analysis be conducted to better understand why some and not other farmers have adopted the system, and their respective level of adoption. To finalize, the whole farm approach used in the economic analysis of the sod based rotation in Southeastern US and in the environmental impact research in the sod based rotation in Colombia is helpful to maintain. Although on many occasions the
150 agricultural research sector has proposed interaction among all system components (annual crop s, pastures, cattle, etc.) be the means of study, it is still not a prevailing approach in the agricultural sciences; instead, the commodity approach prevails (Thomson and Bahaddy, 1994). Maintaining a whole farm approach permits results from crop specific studies to be used in system based evaluations, which are crucial for understanding the impacts of the whole system in the longer term. Furthermore, the whole farm approach is important because, in practice, the selection of rotation and tillage system by a producer depends on the net returns for the whole system and not just on individual components in isolation of other elements (Meyer Aurich et al. 2006).
151 APPENDIX A LETTERS AND SURVEY USED TO ASSESS ADOPTION POTENTIAL OF SOD BASED ROTATIONS Prenotice le tter
153 Survey mailed to farmers in the Southeastern US
158 Postcard reminder
159 APPENDIX B DESCRIPTION OF THE LINEAR PROGRAMMING MODEL The model used in this analysis is an adjusted version of the ECOSAUT model develope d by Quintero et al. (2006). The ECOSAUT model was built on the basis of the relationship between decision variables and decision alternatives. Decision variables capacities, farmer considerations, or regional policies. Decision alternatives refer to activities that are carried out in the system to maintain its functioning. Table B 1 presents the principal decision variables and alternatives considered in this analysis . Once t hese variables and alternatives were interrelated, the ECOSAUT matrix in an Excel spreadsheet was modified . The resultant matrix is represented in Table B 1 . Based on information provided to the model on the costs and revenues of each of the decision varia bles each year (i.e. costs and revenues of each crop in a given rotation), the model maximized the net revenue over ten years. For computing the net revenue, the model is limited by the available land specified in the matrix.
160 Table B 1. Representation of the linear programming model used in this analysis DECISION ALTERNATIVES Sod based rotations (peanut cotton bahiagrass) (hectares) Conventional peanut cotton rotation (hectares) Cow calf operation (hectares) Cash flow transfer between yea r s SBR 1 SBR 2 SBR 3 SBR 4 1 2 3 4 5 6 7 8 9 10 Net revenues (objective function) Annual net revenue Y ear 1 Y ear 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9 Year 10 Land availability (ha) Maximum allowed value hectares Dollars per year Minimum allowed value hectares Dollars per year
161 APPENDIX C SOIL PROFILES DESCRIPTION AND CHARACTERISTICS Horizon A Color: 10YR2/2 Lower depth: 130 cm Bulk density: 0.95 g cm 3 Field capacity: 55.7 Available water content (%): 19.7 Total porosity (%): 58.1 Macropores (%): 18.5 Mesopores (%): 12.5 Micropores (%): 34.1 Soil compaction: 9 1.3 Sand (%): 46.2 Silt (%): 22.5 Clay (%): 31.3 Organic matter (%): 9.58 Horizon AB Color: 10YR3/2 Lower depth: 150 cm Figure C 1. Soil profiles description and characteristics in site 1
162 Horizon A Color: 10YR2/1 Lower depth: 30 cm Bulk densit y: 1.53 g cm 3 Field capacity (%): 27.38 Available water content (%): 10.98 Total porosity (%): 36.74 Macropores (%): 6.75 Mesopores (%): 10.92 Micropores (%): 24.9 Soil compaction: 97.4 Sand (%): 17.33 Silt (%): 40.30 Clay (%): 47.33 Organic matter (%): 1.92 Horizon B Color: 10YR5/6 Lower depth: 110 cm Bulk density: 1.49 g cm 3 Field capacity (%): 31.60 Available water content (%): 9.15 Total porosity (%): 42.88 Macropores (%): 11.48 Mesopores (%): 9.20 Micropores (%): 33.67 Soil compaction: not measure at this depth Sand (%): 26.21 Silt (%): 51.66 Clay (%): 22.13 Organic matter (%): 1.66 Horizon C Color: 10YR5/6 Lower depth: 150 cm Bulk density: 1.36 g cm 3 Field capacity (%): 34.95 Available water content (%): 7.65 Total porosity (%): 46.45 Macropo res (%): 7.75 Mesopores (%): 7.68 Micropores (%): 37.09 Soil compaction: 87 Sand (%): 18.15 Silt (%): 23.91 Clay (%): 57.94 Organic matter (%): 1.06 Figure C 2. Soil profiles description and characteristics in site 2 (Inceptisol)
163 APPENDIX D STRUCTURE FOR RUNOFF AND SOIL LOSS SAMPLING Figure D 1. Galvanized metallic structure for runoff and soil loss sampling Figure D 2. Metallic structure for runoff and soil loss collection in different crops Slope direction Distance below ground (10cm) PV C pipe to transport water and fina soil particles into the container 220 l bucket Metallic structure to capture soil loss and to enroute runoff water toward PVC pipe Sieve to avoid sol particles in the runoff container Distance from soil (10cm)
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176 BIOGRAPHICAL SKETCH Marcela Quintero graduated from Javeriana University in Colombia with a b achelor's degree in ecology. She then began working at the International Center for Tropical Agriculture (CIAT) in 2002. While a research assistant at CIAT, she earned her ork experience has been on quantifying and valuing the environmental and economic externalities produced by agriculture. Most of her research has been conducted in Colombia, Peru, and Ecuador. In 2010, she enrolled in a doctorate program at the Department of Agronomy of the University of Florida; while in this program she divided her time in courses from this department and the Food and Resource Economics Department. Her research work focused on the environmental impacts, economic performance and adoption p otential of mixed crop livestock systems in North Florida and Colombia (South America). She received her doctoral degree in summer of 2014. Currently, she is the Ecosystem Services Theme Leader at the Decision and Policy Analysis R es earch Area of the Internation al Center for Tropical Agriculture.