B1123 Summary of Precision Farming Practices and Perceptions of

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Mississippi Bulletin 1123 August 2002 Summary of Precision Farming Practices and Perceptions of Mississippi Cotton Producers Steven W. Martin Agricultural Economist MSU Extension Service Delta Research and Extension Center Fred Cooke, Jr. Economist Delta Research and Extension Center For more information, contact Dr.

Martin by telephone at (662) 686-3234 or by e-mail at smartin@ext.msstate.edu. Bulletin 1123 was published by the Office of Agricultural Communications, a unit of the Division of Agriculture, Forestry, and Veterinary Medicine at Mississippi State University. Summary of Precision Farming Practices and Perceptions of Mississippi Cotton Producers Featuring results from the 2001 Southern Precision Farming Survey Lead Researchers Alabama 3 Bob Goodman Cotton Incorporated 3 Jeanne Reeves Florida 3 Sherry Larkin Georgia 3 Don Shurley Mississippi 3 Steve Martin North Carolina 3 Michele Marra Tennessee 3 Roland Roberts Sponsored by Cotton Incorporated and the respective land-grant universities.

Executive Summary . . .

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. 1 Introduction . .

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. . . . . . . . . . . . . . . . . . . 2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Survey Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Questions for Adopters (Questions 1-19) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Questions for Adopters and Nonadopters (Question 20-41) . . . . . . . . . . . . . . . . . . 4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Comparisons of Survey Data with Secondary Data Sources . . . . . . . . . . . . . . . . . . 5 Adopter Responses about Precision Farming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Use of Precision Farming Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Decision-Making Value of Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Factors Influencing Use of Precision Farming . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Soil Sampling Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Variable Rate Input Application Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Precision Farming Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Information Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Precision Farming Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Changes in Profit and Environmental Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Adopter and Nonadopter Responses about Precision Farming . . . . . . . . . . . . . . . . 8 Future of Precision Farming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Perceived Price of a Cotton Yield Monitoring System . . . . . . . . . . . . . . . . . . . . 8 Willingness to Buy a Yield Monitoring System . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Respondent and Farm Characteristics for Adopters and Nonadopters . . . . . . . . . . 9 Farm Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Respondent Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Appendix I: The Questionnaire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Appendix II: Tables of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 C ONTENTS Precision farming is being hailed as a set of new technologies that promise private economic gains and societal environmental benefits. These new technolo- gies are used to identify and measure within-field variability and its causes, prescribe site-specific input applications that match varying crop and soil needs, and apply the inputs as prescribed. Reduction of input levels, increased efficiency of inputs, and proper timing of inputs can reduce costs while increasing yields and returns. Extensive research has been conducted in low- value grain crops for which yield monitors have been commercialized. The use of precision technology for cotton (a higher valued crop) is more limited because accurate yield monitors have only recently become commercially available. Because cotton is an important high-value crop in Mississippi, an assessment of the use of precision farming practices, an investigation into the factors that influence adoption of precision farming technologies, and an evaluation of the likelihood that cotton producers will adopt newly developed yield monitoring systems would provide important informa- tion for Mississippi cotton producers and agribusinesses alike. The adoption of precision farming technologies depends on the characteristics of the decision maker, the farm, and the cotton market. The 1997 Census of Agriculture revealed 1,700 cotton producers in Mississippi. Overall characteristics of Mississippi farms as reported in the 1997 Census were 65% full ownership of farm land, 96% family/partner ownership of the farming operation, and 3% corporate ownership. According to the census, 6.8% of the farms contained 1,000 acres or more. Planted acres of cotton in Mississippi have ranged from 950,000 to 1.3 million acres over the last five years. Statewide cotton yields have averaged 753 pounds per acre for the period 1996- 2000. The future of precision farming in cotton production depends on how producers view this set of new technologies and how willing they are to improve current management practices. This study had two objectives: (1) to determine Mississippi cotton producers 9attitudes toward and cur- rent use of precision farming technologies; and (2) to examine Mississippi cotton producers 9 willingness to pay for a cotton yield monitoring system. Amail survey of cotton producers located in Alabama, Florida, Georgia, Mississippi, North Carolina, and Tennessee was conducted in January and February of 2001 to establish the current use of precision farming technolo- gies in these southeastern states. This report provides information dealing with the Mississippi portion of the survey. Results indicate cotton producers are listening to crop consultants, university extension and research per- sonnel, and farm dealers in making decisions about precision farming. Most responding cotton producers use computers for farm management decisions, and they believe precision farming will be profitable in the future. Those producers who adopt these technologies do so to increase profit. The top four precision farming technologies being used by adopters were soil survey maps, soil grid sampling, soil sampling by management zones, and variable rate fertilizer application. Responding producers indicated less willingness to purchase precision farming equipment (yield monitors) as price increased. Mississippi Agricultural and Forestry Experiment Station 1 E XECUTIVE S UMMARY Summary of Precision Farming Practices and Perceptions of Mississippi Cotton Producers 2 Summary of Precision Farming Practices and Perceptions of Mississippi Cotton Producers The Mississippi State Planning Budgets (MSPB) show the cost of cotton production in Mississippi using conventional practices to be as much as $587 per acre, depending on soil type and irrigation practices. MSPB costs do not include land costs, overhead costs, or any return to management. With cotton prices extremely low, producers are continuously looking for ways to reduce these costs. Reduction of input levels, increased efficiency of inputs, and proper timing of the inputs may reduce costs while increasing yields and returns. Precision farming is being hailed as a set of new technologies that promise private economic gains and societal environmental benefits. These new technolo- gies are used to identify and measure within-field variability and its causes, prescribe site-specific input applications that match varying crop and soil needs, and apply the inputs as prescribed. Thus far, most pro- ducers have made only modest investments in precision farming technologies (Lowenberg-DeBoer, 1999). A review of literature by Lambert and Lowenberg- DeBoer (2000) summarizes the profitability of precision farming. Seventy-three percent of the studies they reviewed found precision farming to be profitable. Early studies that investigated the economic potential of precision farming showed mixed results. Lowenberg-DeBoer et al. (1994) found site-specific management (i.e., precision farming) of phosphorous and potassium on corn to be unprofitable with the exception of fields with low soil tests. Beuerlein and Schmidt (1993) also determined that precision farming was unprofitable on corn and soybeans when managing phosphorus and potassium but acknowledged more efficient use of fertilizer as a resulting benefit. Fiez et al. (1994) suggested that precision farming is poten- tially profitable for managing nitrogen on wheat, while Malzer (1996) and Schnitkey et al. (1997) agreed it is profitable on the majority of corn and soybean field tri- als conducted for their studies where phosphorous and potassium were controlled. Hammond (1993) reported inconclusive results on the profitability of variable rate technology for potatoes when applying phosphorous and potassium. Mixed results concerning the profitabil- ity of variable rate technology when managing nitrogen on corn were reported by Snyder et al. (1997). While these studies provide some insight into the economic value of precision agriculture, the fact remains that little is actually known about the economic value of this new technology. A search for precision agriculture economics via the AGRICOLA database returned only 18 publications in which these topics were mentioned within the last 10 years. It is evident even to some of the 18 authors that little economic analysis has been performed in precision farming. According to Leboeuf, in HortTechnology , cAs valu- able field experience increases (in Precision Agriculture), successful applications of management practices are being identified even though few are ade- quately documented with economic benefits. d Extensive research has been conducted in low- value grain crops for which yield monitors have been commercialized. The use of precision technology for cotton (a higher valued crop) is more limited because accurate yield monitors have only recently become commercially available. Because cotton is an important high-value crop in Mississippi, an assessment of the use of precision farming practices, an investigation into the factors that influence adoption of precision farming technologies, and an evaluation of the likelihood that cotton producers will adopt newly developed yield- monitoring systems would provide important information for Mississippi cotton producers and agribusinesses alike. Cotton is produced in Mississippi on a wide range of soils with varying yield potentials. Topsoil, rooting depth, water-holding capacity, texture, and other soil characteristics vary within a field and can cause yields to vary across a field. Though accurate cotton yield monitors have only recently become commercially available, other precision farming technologies have been available to cotton farmers for some time. These precision farming services can be custom hired from consultants and vendors for a fee or implemented by the producers. The adoption of precision farming technologies depends on the characteristics of the decision maker, the farm, and the cotton market. The 1997 Census of Agriculture revealed 1,700 cotton producers in Mississippi. Overall characteristics of Mississippi farms as reported in the 1997 Census were 65% full ownership of farm land, 96% family/partner ownership of the farming operation, and 3% corporate ownership. According to the census, 6.8% of the farms contained 1,000 acres or more. Planted acres of cotton in Mississippi have ranged from 950,000 to 1.3 million I NTRODUCTION Mississippi Agricultural and Forestry Experiment Station 3 acres over the last five years. 2001 was projected to have the largest planted acreage in some time; it was projected at 1.5 million acres. Statewide cotton yields have averaged 753 pounds per acre for the period 1996- 2000. The future of precision farming in cotton produc- tion depends on how producers view this set of new technologies and how willing they are to improve cur- rent management practices. Hudson and Hite suggest that while the newness of the technology may con- tribute to low adoption rates, the uncertainty surrounding the benefit-cost ratio is more likely the limiting factor to adoption of precision farming prac- tices. Swinton and Lowenberg-DeBoer (1998) caution that the early profits of technology adoption will go only to those producers with strong technical and man- agerial skills. A need exists to assess producers 9 experiences with a variety of precision farming tech- nologies and to determine what benefits they have received or expect to receive from using these tech- nologies. Such an assessment is needed to appraise the present status and future prospects for adoption of pre- cision farming technologies by cotton producers in Mississippi. Objectives 4 This study had two main objec- tives: (1) to determine Mississippi cotton producers 9 attitudes toward and current use of precision farming technologies; and (2) to examine Mississippi cotton producers 9 willingness to pay for a cotton yield moni- toring system. M ETHODS Survey Methods A mail survey of cotton producers located in Alabama, Florida, Georgia, Mississippi, North Carolina, and Tennessee was conducted in January and February of 2001 to establish the current use of preci- sion farming technologies in these southeastern states. This report provides information dealing with the Mississippi portion of the survey. A questionnaire was developed to query producers about their attitudes toward and use of precision farm- ing technologies (Appendix 1). The questionnaire was previously pretested on two producers in Tennessee by the University of Tennessee researchers involved in this study, and their suggestions were incorporated into the final version. Following Dillman 9s general mail survey procedures, the questionnaire, a postage-paid return envelope, and a cover letter explaining the purpose of the survey were sent to each producer. The initial mail- ing of the questionnaire was on January 16, 2001, and a reminder postcard was sent one week later on January 23, 2001. A follow-up mailing to producers not responding to previous inquiries was conducted three weeks later on February 15, 2001. The second mailing included a letter indicating the importance of the sur- vey, the questionnaire, and a postage-paid return envelope. Producers were instructed to return their questionnaires without filling them out if they were not cotton producers. A mailing list of 1,334 potential Mississippi cotton producers for the 1999-2000 season was furnished by the Cotton Board in Memphis, Tennessee (Skourpa, 2000). Of the 1,334 questionnaires mailed, 24 were returned undeliverable and 28 indicated they were not cotton farmers or had retired, giving a total of 1,282 cotton producers in Mississippi. Of those who responded, 262 individuals provided data. Assuming the remaining nonrespondents to the survey were active cotton producers, the usable response rate was 20%. 4 Summary of Precision Farming Practices and Perceptions of Mississippi Cotton Producers In question 1 of the questionnaire, producers were asked to indicate the number of years they had used precision farming technologies on cotton, corn, peanuts, rice, soybeans, tobacco, and wheat. If they had not used any of the technologies, they were instructed to proceed to question 20. The precision farming tech- nologies included yield monitoring with global positioning systems (GPS); yield monitoring without GPS; yield monitoring without a yield monitor; grid soil sampling; management-zone soil sampling; remote sensing through aerial photography; remote sensing through satellite imagery; soil survey maps; mapping topography, slope, soil depth, and other field attributes; plant tissue testing; on-the-go sensing; and variable rate application of nitrogen, phosphorous, potassium, lime, seed, growth regulators, defoliants, fungicides, herbi- cides, insecticides, and irrigation. Producers were asked to identify the decision-making value of the tech- nologies they used and the factors that prompted their decision to practice precision farming. Producers were questioned about their soil sam- pling techniques, use of variable rate application tech- nology to apply different inputs, and how variable rate application affected total input use and cotton yields. They were asked to list precision farming equipment they presently owned or leased and problems they encountered with the equipment. For the precision farming technologies producers 9 had used or investigated, they were asked to rate the importance of several information sources in learning about those technologies. They were also asked to iden- tify the off-farm precision farming services used or employed on the farm and the cost of hiring those serv- ices. Producers who had used precision farming tech- nologies were asked if they found them profitable on their fields. In the case of unprofitable precision farm- ing techniques, producers were asked to list the technologies they planned to discontinue. Producers were also asked to indicate whether they experienced improvements in environmental quality through preci- sion farming and to list those improvements observed. Questions for Adopters (Questions 1-19) Questions for Nonadopters (Questions 20-41) Both adopters and nonadopters of precision farm- ing were asked to offer their opinions on the future of precision farming, to indicate whether they would pre- fer to own or lease equipment, and to give their best estimate of the typical purchase price of a cotton yield monitoring system with GPS. They were asked to indi- cate their opinions regarding the importance of precision farming five years in the future. Producers were asked to provide acreage data, pri- mary county of farm, estimated yields for all crops grown in 1999 and 2000, and annual average yield vari- ability of a typical field for each of the crops they grew. They were also queried on their cotton equipment, age, education, computer use, farm finances, and farm plan- ning goals. To obtain information about cotton producers 9will- ingness to pay for a yield monitoring system (Objective 2), the mailing list from the Cotton Board was ran- domly divided into six equal groups with each group given a different dollar amount in the willingness to pay questions. The dollar amounts were $4,500, $6,000, $7,500, $9,000, $10,500, and $12,000. Respondents were asked in question 30 to indicate if they currently owned a cotton picker and the size of the picker. In question 31, they were asked to establish their willingness to purchase a cotton yield monitoring system for their existing cotton picker. Question 32 asked respondents to indicate if they were considering purchasing or leasing a new cotton picker and the size of the picker. The purpose of question 33 was to dis- cover producers 9 willingness to purchase an optional cotton yield monitoring system for an additional cost if they were purchasing or leasing a new cotton picker. The price of a cotton yield monitoring system at the time of the survey was $9,200 for a system with a mon- itor, a GPS receiver, sensors on two chutes of a four- or five-row picker, and the ability to estimate lint yield within 4% of actual yields. The price of an additional sensor for a six-row picker was $1,285 (Ag Leader, 2000). Mississippi Agricultural and Forestry Experiment Station 5 R ESULTS Results are presented in five sections. The first sec- tion compares several characteristics of the respondents and their farming operations with data from the 1997 Census of Agriculture (Mississippi results) and the National Agricultural Statistics Service. The second section presents information on the use of precision farming practices in Mississippi. Where appropriate, the responses of precision farming adopters and non- adopters are compared. In the third section, demographic and farm characteristics are compared for precision farming adopters and nonadopters. The final two sections present the characteristics of the typical precision farming technology adopter and nonadopter. The distribution of cotton farmers across Mississippi counties reported in Table 1 (Appendix II) corresponded closely with the distribution of respon- dents across counties. In 1997, more than 70% of the cotton producers were located in the Delta region of Mississippi (U.S. Department of Agriculture). Of the responding farmers, 72% were from the Delta region of Mississippi. The average age of a typical respondent was younger than the average age of cotton producers reported in the census. The average age of cotton farm- ers reported in the survey was 51 years. In 1997, the average age of Mississippi producers was 55 years (U.S. Department of Agriculture). Respondents ranged in age from 21 to 89 years. In 1997, 77% of Mississippi cotton producers reported farming as their primary source of income (U.S. Department of Agriculture), compared with 83% of survey respondents. Survey respondents reported planting averages of 913 and 962 acres of cotton in 1999 and 2000, respec- tively, compared with an average planted acreage of 559 acres in 1997 (U.S. Department of Agriculture). In 1999 and 2000, Mississippi cotton yields were esti- mated at 702 and 654 pounds per acre (Mississippi Agricultural Statistics Service), respectively, while sur- vey respondents reported average yields of 750 and 700 pounds per acre, respectively. Thus, acreage reported by survey respondents were much higher in 1999 and 2000 than reported in the 1997 Census, but yields reported by respondents were much closer to estimates from the Mississippi Agricultural Statistics Service. Use of Precision Farming Technologies Adopting producers were asked to indicate the number of years they had used each precision farming technology for cotton and other crops (survey question 1). Descriptive statistics about the number of years Mississippi producers have used some form of precision farming technology on fields of cotton, corn, soybeans, and wheat are reported in Tables 2-6. Based on the num- ber of responses presented in Table 2, the top four technologies being used by adopters were soil survey maps, soil grid sampling, soil sampling by management zones, and variable rate fertilizer application. When considering the average years of use, these technologies were also among the most used with soybeans being the leading commodity followed by cotton. Decision-Making Value of Technologies Adopters were asked to rate the decision-making value of precision farming on a scale of 1 (not impor- tant) to 5 (very important) as presented in Table 7 (Appendix II) (survey question 2). cImproving yields d was ranked as the most important criteria for adopting precision farming practices. cDiscovering the need for drainage d was also very important to the majority of adopters. cQuit farming a portion of a field or an entire field d was not very important to adopters. However, producers who have adopted precision farming tech- nologies considered all the possible benefits of available technology at least moderately important by ranking all of the other items an average of three or better. Factors Influencing Use of Precision Farming Precision farming adopters were asked to rate on a scale of 1 (not important) to 5 (very important) several factors that went into their decision to adopt precision farming technologies (survey question 3). Adopters reported that profit was the most important factor prompting them to adopt precision farming with 74% of Comparisons of Survey Data with Secondary Data Sources Adopter Responses about Precision Farming respondents considering it very important and only 2% indicating it was not important to their decision (Table 8, Appendix II). The fear of being left behind was least likely to persuade producers to practice precision farm- ing. Environmental benefits were also very important to adopters with 64% ranking them 4 or higher. Soil Sampling Technologies Questions 4 through 8 of the survey questioned adopting producers about their soil sampling practices. Forty-two percent of responding adopters did the major- ity of their soil sampling within management zones and 35% did grid soil sampling, but only 8% pulled cores from grids within management zones (Appendix II, Table 9). Fifteen percent of adopters used none of the three precision-sampling choices listed in question 4. The majority (60%) of responding adopters in Mississippi used consultants to collect their soil samples (Appendix II, Table 9). Twenty-two percent used a fer- tilizer or chemical dealer to collect samples, while only 17% collected the samples themselves. Seventy percent of adopters pulled soil cores from around the center point of the grid or management zone, while only 30% of adopters collected cores randomly within a grid or management zone. The average management zone size was 19 acres and ranged from 1-100 acres (Appendix II, Table 10). On average, 10 soil cores were taken per management zone, with a range of one to 100 cores per zone. The typ- ical grid size for adopters averaged 11 acres and ranged from 1-40 acres. On average, six soil cores were taken per grid, ranging from zero to 30 cores. Variable Rate Input Application Technologies Cotton producers who had adopted some form of precision farming technology were asked in question 9 about their use of variable rate application technologies on cotton. The majority of adopters did not use variable rate application technologies on cotton (Appendix II, Table 11). Forty percent of responding adopters used variable rate phosphorus and potassium application, fol- lowed by variable rate lime application (30%), variable rate nitrogen application (25%), and variable rate growth regulator and defoliant application (18%). Few responding adopters had used variable rate technology for manure application, nematicide application, or irri- gation (5% or less). Of those responding adopters who used variable rate lime and/or phosphorus and potassium application, the majority (+63%) reported decreases in input usage (Appendix II, Table 11). Forty-five percent of respond- ing adopters reported an increase in total input use with variable rate nitrogen application. Another 27% reported a decrease in inputs, while 27% saw no affect on total nitrogen use. Total growth regulator use also decreased with variable rate application for 100% of responding adopters, while 43% of adopters experi- enced a decrease in defoliant use. Adopters were asked to indicate how their cotton yields changed following variable rate application (sur- vey question 10). Thirty-nine percent of the responding adopters experienced an increase in yields, 14% reported a decrease, and 47% indicated no change in cotton yields (Appendix II, Table 12). Poor weather conditions in 1999 and 2000 may have affected yield responses. In survey question 11, adopters were asked to indicate the magnitude of the change in yields. Sixteen adopters reported an average increase of 32 pounds of lint per acre. Responses ranged from zero to 100 pounds (Appendix II, Table 12). Precision Farming Equipment Adopting producers were asked to list in question 12 any precision farming equipment they presently owned or leased, in what year it was purchased, and the purchase or lease price. Adopters were also given an opportunity to list any problems they may have encoun- tered with the equipment. Yield monitors and GPS receivers were the most commonly listed products. Most of the equipment was purchased since 1998. Common problems listed included broken wires and the inability to receive GPS satellite signals. In general, very little information was reported on precision farm- ing equipment. Information Sources In survey question 13, adopters were asked to rate the helpfulness (1 = not helpful to 5 = very helpful) of different information sources in learning about the pre- cision farming technologies they had used or investigated. Average scores for farm dealers as a source of information were highest for learning about variable rate lime application (4.06), variable rate phosphorous and potassium application (3.95), and soil grid sampling (3.32) (Appendix II, Table 13). Information gathered from farm dealers was not helpful for mapping topogra- phy (1.70); variable rate herbicide application (1.80); plant tissue sampling (1.89); aerial photography (2.00); 6 Summary of Precision Farming Practices and Perceptions of Mississippi Cotton Producers satellite imagery (2.00); on-the-go sensing (2.00); vari- able rate growth regulator, defoliant, fungicide, and insecticide application (2.00); or soil survey maps (2.31). In Table 14 (Appendix II), results show that crop consultants were most helpful in learning about soil sampling in management zones (4.69), grid soil sam- pling (4.48), variable rate nitrogen application (4.27), variable rate lime application (4.21), variable rate phos- phorus and potassium application (4.21), and plant tissue sampling (4.07). Responders rated crop consult- ants as somewhat helpful in all the listed areas. Adopters considered the Extension Service and uni- versity experts most helpful as sources of information in learning about soil survey maps (3.71); soil grid sampling (3.71); mapping topography, slope, soil depth, etc. (3.70); soil management zones (3.69); and variable rate insecti- cide application (3.67). These sources were least helpful in farmers 9 efforts to learn about yield monitoring with and without a yield monitor (Appendix II, Table 15). Other farmers were not generally rated as helpful sources of information in learning about precision farm- ing technologies. Average scores were highest for yield monitoring without GPS (3.29), yield monitoring with GPS (3.17), and soil grid sampling (2.57) (Appendix II, Table 16). Other farmers were reported as helpful in no other areas. The majority of adopters indicated that trade shows were not helpful sources of information in learning about precision farming technologies (Appendix II, Table 17). Similarly, the Internet and news media were not considered helpful sources of information (Appendix II, Tables 18 and 19). Table 20 (Appendix II) summarizes the average scores for sources of information about all precision farming technologies considered across all responding adopters. Crop consultants (3.62), Extension Service and university personnel (3.28), and farm dealers (2.58) were the most helpful. Other farmers (1.90), Internet (1.69), trade shows (1.38), and the news media (1.13) were the least helpful sources for learning about preci- sion farming technologies. Precision Farming Services In question 14 of the survey, adopting producers were asked if they used the services of a farmers 9coop- erative, a technical consultant, a custom applicator, Extension Service, or other agencies to perform any pre- cision farming task on their farms. Sixty-three percent of responding adopters had used off-farm precision farming services (Appendix II, Table 21). Precision- farming adopters who had used off-farm precision farming services were asked to identify the services they had used or employed and the cost of those services (survey question 15). The majority of adopters reported receiving management and technical advice concerning the precision farming technologies they used (Appendix II, Table 22). The largest majority (100%) of responding adopters received advice concerning grid soil sampling. The average cost of advice on grid soil sampling was $3.36 per acre. Average cost for advice on soil survey maps was $2.67 per acre; for variable rate nitrogen application, $2 per acre. Almost all responding adopters indicated that they would purchase the advice again. The most popular custom services hired by adopters are presented in Table 23 (Appendix II). Grid soil sam- pling was most popular with 89% of those who had adopted this technology having hired this service. The average costs of custom hiring the services were $7.92 per acre for grid soil sampling, $7.11 per acre for vari- able rate phosphorous and potassium application, and $6.80 per acre for variable rate lime application. Most of the responding farmers indicated they would purchase the service again. Responders indicated they would not purchase services for GPS and variable rate nitrogen application again. Changes in Profit and Environmental Quality Questions 16 through 19 of the survey dealt with adopter perceptions about the economic and environ- mental consequences of precision farming. Seventy-two percent of responding adopters thought precision farm- ing was profitable (question 16) on their fields (Appendix II, Table 24). Adopters who found precision farming unprofitable were given an opportunity in ques- tion 17 to list the technologies they planned to discontinue; however, few farmers responded and each gave a varied answer. Only 33% of adopters thought they had experienced an improvement in environmental quality (question 18) as a result of precision farming (Appendix II, Table 24). In question 19, adopters were given an opportunity to list the improvements in envi- ronmental quality they had observed. Among their responses were cless nitrogen use, d clower fertilizer rates, d cless fertilizer run-off, d cbetter drainage, d cleav- ing out areas that are not profitable, d ccheaper in the long run, d cmore no-till, d and cless herbicide injury. d Mississippi Agricultural and Forestry Experiment Station 7 Future of Precision Farming Questions 20, 21, and 23 asked all producers about the future of precision farming. They were asked in ques- tions 20 and 21 if they thought precision farming would be profitable for them to use in the future, and if so, would they prefer to own or rent their equipment. Eighty-eight percent of adopting producers and 61% of nonadopting producers thought precision farming would be profitable for them to use in the future (Appendix II, Table 25). For those respondents who believed it would be profitable, 61% of adopters and 53% of nonadopters would prefer to own the precision farming equipment. Question 23 gave respondents an opportunity to rate the importance of precision farming for several crops five years in the future. The level of importance ranged from 1 (not important) to 5 (very important). Adopters consis- tently rated the importance of precision farming five years in the future higher than did nonadopters (Appendix II, Table 27). For cotton production, the aver- age scores for adopters and nonadopters were 4.10 and 3.53, respectively; for corn production, they were 3.87 and 3.33, respectively; for soybean production, they were 3.48 and 2.89, respectively; for rice production, they were 3.96 and 3.01, respectively; and for wheat pro- duction, they were 3.39 and 2.52, respectively. Perceived Price of a Yield Monitoring System In question 22, producers were asked to report their best estimate of the typical purchase price for a cotton yield monitoring system with GPS for their area. The average purchase price given by adopters was $8,182, while the average price given by nonadopters was $7,441 (Appendix II, Table 26). These average perceived prices were less than the actual price ($9,200) at the time of the survey for a cotton yield monitoring system that included a monitor, a GPS receiver, and sensors on two chutes of a four- or five-row picker (Ag Leader Technology, 2001). Willingness to Buy a Yield Monitoring System In question 30, all cotton farmers were asked if they owned a cotton picker; if so, they were asked to indicate whether they owned a four-, five-, or six-row picker. The purpose of this question was to determine if the respon- dent was a candidate for retrofitting a yield monitoring system on an existing picker. Eighty-one percent of adopters and 86% of nonadopters owned a cotton picker (Appendix II, Table 28). Of the adopters who responded to the second part of question 30, 78% owned a four-row cotton picker, 13% owned a five-row picker, and 9% owned a six-row picker. Eighty-seven percent of responding nonadopters owned a four-row picker, 7% owned a five-row picker, and 5% owned a six-row picker. Table 29 (Appendix II) reports respondents 9willing- ness to purchase a yield monitoring system for their four- or five-row cotton picker at specified dollar amounts (survey question 31). Clearly, smaller percentages of respondents were willing to purchase the yield monitor- ing system and larger percentages were unwilling to purchase the system as the price increased. The percent- age of respondents in the cDon 9t know d and cDon 9t own a four- or five-row picker d remained about the same as the price increased. Survey question 32 asked all cotton farmers if they were considering purchasing or leasing a new cotton picker. The purpose of this question was to determine if the respondent was a candidate for purchasing an optional yield monitoring system with the new picker. Only 25% of responding adopters and 13% of respond- ing nonadopters were considering purchasing or leasing a new picker (Appendix II, Table 28). Sixty percent of the responding adopters were considering purchasing or leasing a new four-row picker, 7% a five-row picker, and 33% a six-row picker. Fifty-eight percent of responding nonadopters were considering a four-row picker, 8% a five-row picker, and 34% a six-row picker. Table 30 (Appendix II) reports respondents 9willing- ness to purchase or lease an optional yield monitoring system when they purchase or lease a new four-, five-, or six-row cotton picker at specified dollar amounts (survey question 33). The data show a trend downward in the percentage of farmers who would be willing to purchase an optional yield monitoring system as the price increases. The trend upward in the percentage of respon- dents who were unwilling to purchase or lease the system is not as clear as in the case of retrofitting a yield monitoring system to an existing picker. In this case, the percentage of respondents in the cDon 9t know d and cDon 9t intend to purchase or lease a new picker d increases with the price. Nevertheless, the price of a cot- ton yield monitoring system appears to affect farmers 9 willingness to pay for the system. 8 Summary of Precision Farming Practices and Perceptions of Mississippi Cotton Producers Adopter and Nonadopter Responses about Precision Farming Mississippi Agricultural and Forestry Experiment Station 9 Respondent and Farm Characteristics for Adopters and Nonadopters Farm Characteristics Respondents were asked to describe their farm in 2000 (questions 24 through 26). On average, precision farming adopters owned 1,434 acres, share rented 1,150 acres under a two-year or longer rental agreement, and cash rented 1,224 acres under a three-year rental agree- ment. The average nonadopter owned 1,045 acres, share rented 828 acres, and cash rented 1,053 acres for three years (Appendix II, Table 31). Producers were asked to provide the county where the majority of their farm was located (survey question 27). The greatest numbers of responses for precision farming adopters came from Washington County (10 adopters), Leflore County (seven adopters), Bolivar County (five adopters), Coahoma County (five adopters), and Humphreys County (five adopters) (Appendix II, Table 1). Geographically, these responses correlate well with total producers responding to the sur- vey from these counties; there is also a good correlation with the number of producers reported in the 1997 Census of Agriculture (USDA). Producers reported acres planted and estimated yields for the crops they produced in 1999 and 2000 (sur- vey question 28). On average, adopters planted 1,183 acres of cotton in 1999 with yield averaging 803 pounds per acre (Appendix II, Table 33). Nonadopters planted 821 acres per farm in 1999. Cotton yields averaged 732 pounds per acre for nonadopters, which was 71 pounds per acre less than adopters 9 average yield. In 2000 (Appendix II, Table 33), adopters planted 1,175 acres yielding 772 pounds per acre, while nonadopters planted 889 acres yielding 677 pounds per acre. In 1999 and 2000, yields and acreage planted to corn, soybeans, and wheat were, in general, higher for adopters than non- adopters (Appendix II, Tables 32 and 33). Producers were asked to provide annual average yields for the most productive one-third, the average, and the least productive one-third of typical cotton, corn, soybean, and wheat fields they farmed (question 29). Adopters reported similar or higher yields with lower standard deviations than nonadopters for cotton in all three yield categories (Appendix II, Table 34). Reporting for other crops varied between categories. Table 35 (Appendix II) presents producers 9 responses to survey question 34 concerning livestock. About the same percentage of adopters (11%) and non- adopters (17%) reported that they owned livestock. Only 8% of responding cotton producers applied manure to their fields, and this was divided evenly between adopters and nonadopters. Respondent Characteristics Producers were queried about their age, years of farming experience, education, and computer usage (sur- vey questions 35 through 38). The average age (question 35) of a precision farming adopter was 51 years and ranged from 25 to 78 years. Nonadopters averaged 50 years of age, ranging from 21 to 89 years (Appendix II, Table 36). Precision farming adopters had farmed an average of 26 years, while nonadopters had farmed an average of 28 years (survey question 36). However, years of farming ranged from four to 57 years for adopters and three to 70 years for nonadopters (Appendix II, Table 36). The overwhelming majority of adopters (90%) and nonadopters (95%) completed high school (question 37). Adopters averaged more than three years of college, while nonadopters averaged more than two years (Appendix II, Table 37). The majorities of adopters (83%) and nonadopters (81%) owned a com- puter (question 38) (Appendix II, Table 38). Eighty percent of adopters used the computer for farm manage- ment, compared with 58% of nonadopters (question 38). Question 39 asked cotton farmers if farming was their primary source of income (Appendix II, Tables 39, 40, and 41). Farming was the primary source of income for all precision farming adopters. Total household income in 2000 ranged from $50,000 to $149,999 for 53% of adopters and less than $50,000 for 15% of adopters. Eight percent of adopters had household incomes greater than $500,000 in 2000. Farming was also the primary income source for most nonadopters. Total household income in 2000 ranged from $50,000 to $149,999 for 51% of adopters and less than $50,000 for 27% of adopters. Six percent of adopters had household incomes greater than $500,000 in 2000. Producers indicated the one statement that best described their farm planning goal in question 40. Fifty- seven percent of adopters and 56% of nonadopters stated their farm planning goal was to acquire enough farm assets to generate sufficient income for family living (Appendix II, Table 42). Twenty-three percent of adopters wanted to expand the size of their operation by acquiring additional resources, and only 5% of the responding adopters were considering selling the farm and moving to a different career. Fifteen percent of non- adopters wanted to expand the size of their operation, 24% were thinking about retirement and transferring the farm to the next generation, and 5% were considering selling the farm. Ag Leader Technology. 2001. 2001 List Prices. 2202 South Riverside Drive, Ames, Iowa 50010. Beuerlein, J., and W. Schmidt. 1993. Grid soil sampling and fertilization. Ohio State Univ. Ext., Agron. Tech. Rep. 9302. Ohio State Univ., Columbus. Fiez, T.E., B.C. Miller, and W.L. Pan. 1994. Assessment of spatially variable nitrogen fertilizer management in winter wheat. J. Prod. Agric. 7:86-93. Hammond, M.W. 1993. Cost analysis of variable fertility management of phosphorous and potassium for potato production in central Washington. p. 213-228. In P.C. Robert et al. (ed). Soil specific crop management. ASA Misc. Publ. ASA, CSSA, and SSSA. Madison, Wisconsin. Hudson, Darren, and Diane Hite. 2000. Adoption of preci- sion agriculture technology in Mississippi: preliminary results from a producer survey. Department of Agricultural Economics Research Report 2001-001. Mississippi State University. Lambert D., J. Lowenberg-DeBoer. 2000. Precision Farming Profitability Review . Site-Specific Management Center. West Lafayette, Indiana: Purdue University. Leboeuf, J. 2000. Practical applications of remote sensing technology 3 an industry perspective. HortTechnology 10(3):475-480. Lowenberg-DeBoer, J. 1994. Economics of precision farm- ing. Agron. Research Center Field Day, Purdue University. West Lafayette, Indiana. Lowenberg-DeBoer, J. 1999. Risk management potential of precision farming technologies. J. Agric. Appl. Econ. 31,2:275-285. Malzer, G.L. 1996. Profitability of variable rate N on corn. p. 6-7. In Proc. 1996 Information Agric. Conf., Urbana, Illinois. Phosphate and Potash Inst. and Found. for Agron. Res., Norcross, Georgia. Mississippi State University Planning Budgets. 2000. Cotton 2001. Mississippi State University Agricultural Economics Departmental Report 116. Schnitkey, G., J. Hopkins, and L. Tweeten. 1997. An eco- nomic evaluation of precision fertilizer applications on corn-soybean fields. In P.C. Roberts et al. (ed). Proc. 3rd International Conference on Precision Agriculture. ASA Misc. Publ., ASA, CSSA, and SSSA. Madison, Wisconsin. Skourpa, B. 2000. Cotton Board, 871 Ridgeway Loop, Ste. 100, Memphis, Tennessee 38120-4019. Snyder, C., T. Schroeder, J. Havlin, and G. Kluitenberg. 1997. An economic analysis of variable rate nitrogen management. In P.C. Roberts et al. (ed). Proc. 3rd International Conference on Precision Agriculture. ASA Misc. Publ., ASA, CSSA, and SSSA. Madison, Wisconsin. Swinton, S., and J. Lowenberg-DeBoer. 1998. Evaluating the profitability of site-specific farming. J. Prod. Agric. 11:439-446. U.S. Department of Agriculture. 1999. 1997 Census of Agriculture: Mississippi State and County Data. National Agricultural Statistics Service, Washington, D.C. U.S. Department of Agriculture. 2001. National Agriculture Statistics Service. www.nass.usda.gov/tn/grnside3.htm. The objectives of this study were (1) to determine Mississippi cotton producers 9attitudes toward and cur- rent use of precision farming technologies and (2) to examine Mississippi cotton producers 9 willingness to pay for a cotton yield monitoring system. Cotton pro- ducers are confronted every day with information concerning the rapidly growing precision farming industry. Most responding cotton producers use com- puters for farm management decisions and believe precision farming will be profitable in the future. Those producers who adopt these technologies do so to increase profit. Cotton producers are listening to crop consultants, extension and research personnel at uni- versities, and farm dealers in making decisions about precision farming. Responding adopters of precision farming practices planted more cotton acreage and reported higher yields per acre than nonadopters. The top four precision farming technologies being used by adopters were soil survey maps, soil grid sampling, soil sampling by management zones, and variable rate fer- tilizer application. Responding producers indicated less willingness to purchase precision farming equipment (yield monitors) as price increased. As more informa- tion becomes available, cotton producers will have greater opportunities to make more informed decisions about the use of these technologies on their farms. Findings from this and other studies that investigate the current use and future prospects of precision farming technologies are important to cotton producers because they provide the needed information for making better decisions. 10 Summary of Precision Farming Practices and Perceptions of Mississippi Cotton Producers C ONCLUSIONS R EFERENCES Mississippi Agricultural and Forestry Experiment Station 11 A PPENDIX I: T HE Q UESTIONNAIRE \x2\x36RXWKHUQ\x33UHFLVLRQ\x3)DUPLQJ\x36XUYH\ cPrecision farming d involves collecting information about within-field variability in yields and crop needs to assist in determining appropriate input levels and applying that information to your farm fields. This may result in varying input levels within each field. 1. In the table below, write the number of years you have used each technology on each crop . If you have not used any of these technologies, leave the boxes blank and proceed to Question 20. Technology Cotton Corn Peanuts Rice Soybeans Tobacco Wheat Yield monitoring 3 with GPS Yield monitoring 3 without GPS Yield monitoring 3 without a yield monitor Soil sampling 3 grid Soil sampling 3 management zone Remote sensing 3 aerial photos Remote sensing 3 satellite images Soil survey maps Mapping topography, slope, soil depth, etc. Plant tissue testing On-the-go sensing Variable rate nitrogen application Variable rate phosphorous and potassium application Variable rate lime application Variable rate seed application Variable rate growth regulator application Variable rate defoliant application Variable rate fungicide application Variable rate herbicide application Variable rate insecticide application Variable rate irrigation 12 Summary of Precision Farming Practices and Perceptions of Mississippi Cotton Producers 2. Rate the decision-making value of the technologies you have used by circling the number that indicates how important you thought the information was (1 = not important, 5 = very important). Item Not Important Very Important Discovering a need for drainage 1 2 3 4 5 Discovering a need for leveling 1 2 3 4 5 Discovering a need for improved soil tilth 1 2 3 4 5 Maintaining a record of field conditions 1 2 3 4 5 Conducting rental negotiations 1 2 3 4 5 Deciding on the purchase of crop insurance (or establishing crop insurance units) 1 2 3 4 5 Maintaining better yield records 1 2 3 4 5 Maintaining better soil test records 1 2 3 4 5 Maintaining better financial records 1 2 3 4 5 Improving yields 1 2 3 4 5 Reducing N use 1 2 3 4 5 Reducing P&K use 1 2 3 4 5 Reducing herbicide use 1 2 3 4 5 Reducing insecticide use 1 2 3 4 5 Reducing plant growth regulator use 1 2 3 4 5 Reducing fungicide use 1 2 3 4 5 Reducing defoliant use 1 2 3 4 5 Quit farming a portion of a field or an entire field 1 2 3 4 5 3. What was your decision to practice precision farming prompted by? (Rate each item from 1 to 5) Item Not Important Very Important Profit 1 2 3 4 5 Environmental benefits 1 2 3 4 5 Be at the forefront of agricultural technology 1 2 3 4 5 Fear of being left behind 1 2 3 4 5 4. Please check the one item below that describes how you do the majority of your soil sampling. Management zones ____________ Grids within management zones ____________ Grids ____________ None of the other three choices ____________ If you checked cNone of the other three choices, d skip to question 9. 5. What is your average management zone size? __________ acres; typical grid size? ________ acres 6. On average, how many soil cores were taken per management zone? _____; per grid? ______ 7. How were cores collected? (Check the one that applies) ________ Randomly within a grid or management zone ________ Around the center point of the grid or management zone 8. Who collected the soil samples? (Please check the best item) Self ______ Consultant ________ Fertilizer or Chemical Dealer ________ Mississippi Agricultural and Forestry Experiment Station 13 9. For your cotton fields only, please provide the following information. Input Did you use variable rate application technology to apply? (Yes or No) If you used variable rate technology, how did it affect total input use? (Increase, Decrease, Same) N fertilizer P&K fertilizer Lime Manure application Seed Herbicide Insecticide Nematicide Irrigation Fungicide Growth regulator Defoliant 10. Following variable rate application, how did your cotton yields change? (Check one) Increase_______ Decrease _______ Stayed the same ________ 11. If your cotton yields changed, by approximately how much did they change? ______ lint (lb/acre) 12. If you presently own or lease any precision farming equipment, please list the equipment and fill out the table; otherwise go to question 13. If equipment is owned Equipment Name Year Purchased Purchase Price ($) If leased, Lease rate? $ per acre List any problems encountered. a. b. c. d. e. 14 Summary of Precision Farming Practices and Perceptions of Mississippi Cotton Producers 13. For only those precision farming technologies you have used or investigated, please rate the importance of each informatio n source in learning about the precision farming technology by writing a number from 1 to 5 in the corresponding box (1 = not hel pful to 5 = very helpful). 14. Did you use the services of a farmers 9 cooperative, a technical consultant, a custom applicator, extension service, etc. to perform any precision farming task on your farm? Yes ______ No ______ If cYes d, go to question 15; if cNo d, go to question 16. Information Sources Precision Farming Technology Farm Dealers Crop Consultants Extension/ Universities Other Farmers Trade Shows Internet News Media Yield monitoring 3 with GPS Yield monitoring 3 without GPS Yield monitoring 3 without a yield monitor Soil sampling 3 Grid Soil sampling 3 Management Zone Remote sensing 3 aerial photos Remote sensing 3 satellite images Soil survey maps Mapping topography, slope, soil depth, etc. Plant tissue testing On-the-go sensing Variable rate nitrogen application Variable rate phosphorous and potassium application Variable rate lime application Variable rate seed application Variable rate growth regulator application Variable rate defoliant application Variable rate fungicide application Variable rate herbicide application Variable rate insecticide application Variable rate irrigation Mississippi Agricultural and Forestry Experiment Station 15 15. In the table below, please identify which services you used or employed and the cost of these services. Management and Technical Advice Custom Services Hired Precision Farming Technology Did you receive advice? (yes or no) What was the per acre cost? Will you purchase this service again? (yes or no) Did you hire this service? (yes or no) What was the per acre cost? Will you purchase this service again? (yes or no) Yield monitoring 3 with GPS Yield monitoring 3 without GPS Yield monitoring 3 without a yield monitor Soil sampling 3 Grid Soil sampling 3 Management Zone Remote sensing 3 aerial photos Remote sensing 3 satellite images Soil survey maps Mapping topography, slope, soil depth, etc. Plant tissue testing On-the-go sensing Variable rate nitrogen application Variable rate phosphorous and potassium application Variable rate lime application Variable rate seed application Variable rate growth regulator application Variable rate defoliant application Variable rate fungicide application Variable rate herbicide application Variable rate insecticide application Variable rate irrigation 16. Do you find precision farming profitable on your fields? Yes ______ No _________ 17. If precision farming has not been profitable for you, which technologies (if any) do you plan to discontinue? List them ______________________________________________________________ 16 Summary of Precision Farming Practices and Perceptions of Mississippi Cotton Producers 18. Have you experienced any improvements in environmental quality through the use of precision farming technologies? Yes _______ No _________ 19. If you said yes to question 18, please list the improvements you have observed. a. _____________________________ c. ______________________________ b. _____________________________ d. ______________________________ Resume here 20. Do you think it would be profitable for you to use precision-farming technologies in the future? Yes _______ No _________ 21. If you believe it would be profitable, would you prefer to own or rent your equipment? Own ________ Rent __________ 22. What is your best estimate of the typical purchase price of the following precision farming technology in your area? Cotton yield monitoring system with GPS $_________ 23. For each crop you grow listed in the table below, please circle how important you believe precision farming will be five years from now in your state (1 = not important, 5 = very important). Item Not Important Very Important Cotton 1 2 3 4 5 Corn 1 2 3 4 5 Peanuts 1 2 3 4 5 Rice 1 2 3 4 5 Soybeans 1 2 3 4 5 Tobacco 1 2 3 4 5 Wheat 1 2 3 4 5 24. Your 2000 farm size? Acres owned ____ ; Acres share rented ____ ; Acres cash rented ____ 25. If you cash rent, what is the length of your typical cash rental agreement? _______year(s) 26. If you share rent, what is the length of your typical share rental agreement? _______year(s) 27. In what county is most of your farm located? __________________________ 28. Please give the acres planted and estimated yields for each crop you grew in 1999 and 2000. 1999 2000 Crops Acres Planted Yield Acres Planted Yield Cotton lb lb Corn bu bu Peanuts lb lb Rice cwt cwt Soybeans bu bu Tobacco lb lb Wheat bu bu Mississippi Agricultural and Forestry Experiment Station 17 29.Please tell us about the annual average yield variability of a typical field that you farm for each of the crops that you grow. Give estimated yield for the following portions of the field. Cotton Lb/acre Corn Bu/acre Peanuts Lb/acre Rice Cwt/acre Soybeans Bu/acre Tobacco Cwt/acre Wheat Bu/acre Least productive 1/3 Average yield Most productive 1/3 30. Do you currently own a cotton picker? Yes ______ No ______ If yes, check the ones you own. 4-row _______, 5-row _______, 6-row _______ 31. 4 or 5-row cotton pickers owned by farmers can be equipped with a yield monitoring system that includes a monitor, a GPS receiver, sensors on two chutes, and the ability to estimate yields within 4% of actual yields. Would you purchase the yield monitoring system for your 4 or 5-row picker for $9,000 installed? Yes ____ No ____ Don 9t know ___Don 9t own a 4 or 5-row picker ___(Check one) 32. Are you thinking about purchasing/leasing a new cotton picker? Yes ____ No ____ If yes, check the ones you are thinking about purchasing/leasing. 4-row __, 5-row __, 6-row__ 33. When a new cotton picker is purchased/leased , a yield monitoring system can be purchased/leased as an option for an additional cost. Would you purchase an optional yield monitoring system that adds $9,000 to the purchase price of a new 4 or 5-row picker (or a corresponding increase in the lease rate), or $10,285 to the purchase price of a new 6-row picker ($1,285 more for an additional sensor for the larger picker)? Yes ___ No ___ Don 9t know ___ Don 9t intend to purchase/lease a new picker ___ (Check one) 34. Do you own livestock? Yes ___ No ___ Do you apply manure on your fields? Yes ___ No___ Please answer the following questions about the primary decision maker on the farm. Answers to all questions will remain strictly confidential. 35. Age? ___________ 36. Number of years farming? _______ 37. Did you complete high school? ______ If yes, how many years did you go to college? _____ 38. Do you own a computer? Yes ___No ___ Do you use it for farm management? Yes __ No __ 39. Is farming your primary source of household income? Yes ________ No __________ 40. Please check the one statement that best describes your farm planning goal. ___ I want to acquire enough farm assets to generate sufficient income for family living? ___ I want to expand the size of operation through acquiring additional resources? ___ I am thinking about retirement and transfer of farm to the next generation? ___ I am considering selling the farm and moving on to a different career? 41 Please check the category that best reflects your total estimated household income from both farm and non- farm sources in 2000. _____ Less than $50,000 _____ $100,000 to $149,999 ______ $200,000 to $499,999 _____ $50,000 to $99,999 _____ $150,000 to $199,999 ______ $500,000 or greater 42. What percent of your household income is from farming? _______% 18 Summary of Precision Farming Practices and Perceptions of Mississippi Cotton Producers Table 1. Primary county of cotton farm business reported by primary decision maker for Mississippi cotton farms 4 2001 Southern Precision Farming Survey. 1 County 1997 Census Number of Precision Precision farming of Agriculture 2 usable surveys farming adopters nonadopters Benton 9 (.6%) 3 1 (.4%) 0 1 (.6%) Bolivar 97 (6%) 18 (7%) 5 (8%) 13 (7%) Calhoun 67 (4%) 2 (.8%) 1 2 (1%) Carroll 31 (2%) 4 (2%) 2 (3%) 2 (1%) Chickasaw 11 (.7%) 3 (1%) 0 3 (2%) Claiborne 3 (.2%) 2 (.8%) 2 (3%) 1 (.6%) Coahoma 92 (6%) 16 (7%) 5 (8%) 11 (4%) Copiah 4 (.2%) 1 (.4%) 0 1 (.6%) DeSoto 18 (1%) 2 (.8%) 0 2 (1%) Forrest 1 (0%) 1 (.4%) 0 1 (.6%) George 5 (.2%) 3 (1%) 0 3 (2%) Greene 4 (.2%) 1 (.4%) 0 1 (.6%) Hinds 28 (2%) 3 (1%) 0 3 (2%) Holmes 57 (4%) 10 (4%) 3 (5%) 7 (4%) Humphreys 93 (6%) 17 (7%) 6 (10%) 11 (4%) Issaquena 29 (2%) 4 (2%) 0 4 (2%) Itawamba 7 (.4%) 2 (.8%) 0 2 (1%) Lafayette 16 (1%) 1 (.4%) 0 1 (.6%) Leake 9 (.6%) 1 (.4%) 1 0 Leflore 107 (7%) 26 (11%) 7 (11%) 19 (10%) Lowndes 18 (1%) 5 (2%) 0 5 (3%) Madison 42 (3%) 7 (3%) 2 (3%) 5 (3%) Monroe 27 (2%) 4 (2%) 0 4 (2%) Montgomery 36 (2%) 3 (2%) 0 3 (2%) Noxubee 14 (.9%) 4 (2%) 0 4 (2%) Panola 53 (3%) 4 (2%) 2 (3%) 2 (1%) Pontotoc 17 (1%) 4 (2%) 0 4 (2%) Prentiss 8 (.6%) 1 (.4%) 0 1 (.6%) Quitman 56 (4%) 7 (3%) 4 (6%) 3 (2%) Rankin 15 (1%) 2 (.8%) 0 2 (1%) Sharkey 41 (3%) 10 (4%) 2 8 (4%) Sunflower 81 (5%) 10 (4%) 3 (2%) 7 (4%) Tallahatchie 93 (6%) 5 (2%) 4 1 (.6%) Tate 30 (2%) 2 (.8%) 0 2 (1%) Tippah 5 (.5%) 1 (.4%) 0 1 (.6%) Tunica 35 (2%) 8 (3%) 5 (8%) 3 (2%) Union 13 (.9%) 1 (.4%) 0 1 (.6%) Warren 14 (.9%) 1 (.4%) 0 1 (.6%) Washington 123 (8%) 30 (12%) 10 (16%) 20 (11%) Webster 53 (3%) 3 (1%) 0 3 (2%) Yazoo 102 (7%) 13 (5%) 4 (6%) 9 (5%) Total 1564 (+/-100%) 244 (+/-100%) 62 (+/-100%) 182 (+/-100%) 1 Survey question 27. 2 Reported in the 1997 Census of Agriculture, USDA. 3 Numbers in parenthesis indicate the percentage of respondents who gave the associated answer. A PPENDIX II: T ABLES OF R ESULTS Mississippi Agricultural and Forestry Experiment Station 19 Table 3. Years of experience with alternative precision farming technologies for corn reported by Mississippi cotton farms 4 2001 Southern Precision Farming Survey. 1 Technology Number of Average Standard Minimum Maximum responses deviation years years years years Yield monitoring 4 with GPS 2 6 2.83 1.17 1 4 Yield monitoring 4 without GPS 10 3.20 1.93 1 5 Yield monitoring 4 without a yield monitor 5 8.2 5.45 4 17 Soil sampling 4 grid 10 3 2 1 7 Soil sampling 4 management zone 11 10 7.25 1 25 Remote sensing 4 aerial photos 1 25 25 25 Remote sensing 4 satellite images 0 0 0 0 0 Soil survey maps 10 14.50 11.33 1 35 Mapping topography, slope, soil depth, etc. 1 2 2 2 Plant tissue testing 0 0 0 0 0 On-the-go sensing 0 0 0 0 0 Variable rate nitrogen application 3 5.33 4.16 2 10 Variable rate phosphorous and potassium application 6 3.5 3.33 1 10 Variable rate lime application 6 3.83 3.25 1 10 Variable rate seed application 0 0 0 0 0 Variable rate growth regulator application 0 0 0 0 0 Variable rate defoliant application 0 0 0 0 0 Variable rate fungicide application 0 0 0 0 0 Variable rate herbicide application 0 0 0 0 0 Variable rate insecticide a

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