Agricultural & Resource Economics

September 2015

PRECISION FARMING BY COTTON PRODUCERS IN FOURTEEN SOUTHERN STATES

Results From the 2013 Southern Cotton Farm Survey

Xia “Vivian” Zhou, Burton C. English, Christopher N. Boyer, Roland K. Roberts, James A. Larson, Dayton M. Lambert, Margarita Velandia University of Tennessee Lawrence L. Falconer, Steven W. Martin Mississippi State University Sherry L. Larkin University of Florida Krishna P. Paudel, Ashok K. Mishra Louisiana State University Rodrick M. Rejesus North Carolina State University Chenggang Wang, Eduardo Segarra Texas Tech University Jeanne M. Reeves (Cotton Incorporated)

This research is funded in part by Cotton Incorporated, Cary, NC 27513. Funding was also provided by the University of Tennessee, Mississippi State University, University of Florida, Louisiana State University, North Carolina State University, and Texas Tech University’s Experiment Stations. The findings and views expressed in this study are those of the authors and may not represent the institutions that employ us. Research Series 15-001

economics.ag.utk.edu peag.ag.utk.edu

Contents Contents ........................................................................................................................................................................................ iii List of Figures ............................................................................................................................................................................. iv List of Tables ................................................................................................................................................................................ v INTRODUCTION ......................................................................................................................................................................... 1 METHODS ..................................................................................................................................................................................... 6 Survey ........................................................................................................................................................................................ 6 Techniques used to evaluate Survey Data ................................................................................................................. 8 RESULTS ........................................................................................................................................................................................ 8 Comparison of 2013 Survey Data with the 2012 Ag Census ............................................................................. 9 Adoption of Precision Farming Technologies ........................................................................................................ 14 Overall Precision Farming Technologies Use .................................................................................................... 14 Use of Information Gathering Technologies ...................................................................................................... 18 Use of Automatic Section Control ........................................................................................................................... 20 Use of GPS Guidance Systems ................................................................................................................................... 20 Use of Variable Rate Management ......................................................................................................................... 22 Users of Precision Farming Technology Responses Regarding Precision Farming Technologies .. 23 Regarding Lint Quality and environmental benefits ...................................................................................... 23 Regarding Variable-‐Rate Management ................................................................................................................ 25 Cotton Farmer Perceptions about Precision Farming ........................................................................................ 26 Primary Barriers to using Precision Farming Technology .......................................................................... 26 Information Sources ..................................................................................................................................................... 27 Variable Rate Management Cost-‐Share Programs .......................................................................................... 28 Demographic and Farm Characteristics of Respondents .................................................................................. 30 Farm Characteristics .................................................................................................................................................... 30 Other Characteristics of Respondents .................................................................................................................. 33 CONCLUSION ............................................................................................................................................................................. 35 Appendix I: The Questionnaire .......................................................................................................................................... 40 Appendix II: Additional Figures ........................................................................................................................................ 46

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LIST OF FIGURES Figure 1 . H arvested C otton A cres b y C ounty i n t he S outhern P roduction R egion a nd t he P roportion o f A cres i n C otton, b y C ounty, 2 012 Figure 2. Study Area for the 2013 Southern Cotton Farm Survey Figure 3 . C umulative S urvey R esponse R ate f or t he 2 013 S outhern C otton Farm S urvey Figure 4 . S patial D istribution o f S urvey R espondents f or t he 2 013 Southern C otton P recision F arming S urvey Figure 5 . G eographical D istribution o f t he N umber o f C otton P roducers in t he 2 012 C ensus o f A griculture i n S outhern U .S. Figure 6 . C omparison o f A ge D istribution i n t he 2 012 C ensus o f Agriculture i n S outhern U S t o t he 2 013 S outhern C otton P recision Agriculture S urvey Figure 7. Irrigated-‐to-‐Dry Yield Ratio for the 2012 Cotton Growers Responding to Question 13 Figure 8. Number of Cotton Acres on which Automatic Section Control Technology was Used by Respondents for Planting and Spraying Activities over Time Appendix Figure II.1. Adoption of Satellite Imagery, Aerial Photographs, and Soil Survey Maps Appendix Figure II.2. Adoption of Geo-‐referenced Information on Soil Requirements

3 6 7 10 10 11 14 21 47 48

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LIST OF TABLES Table 1. Response Rate for the 2013 Southern Cotton Farm Survey and Number of Cotton Farmers Surveyed for the 2013 Survey Compared with 2012 Ag Census Table 2. Planted Acres for Cotton Production for the 2013 Survey Compared with 2012 Ag Census Table 3. Planted Acres and Yield for Cotton Production Table 4. A Comparison between the Numbers of Precision Farming Technology Users by State for 2013 and 2009 surveys Table 5. Adoption Rates by Precision Farming Technology Category Table 6. Use of Information Gathering Technologies by Cotton Farmers Table 7. Use of GPS Guidance Systems Table 8. Use of Variable Rate Input Management Table 9. Importance Rating of Reasons to Practice Precision Farming Table 10. Perceptions about the Effect of Variable Rate Input Application on Yield Table 11. Input Change after Variable Rate Application Table 12. Primary Barrier to Using Precision Farming Today Table 13. Precision Farming Information Sources Table 14. Importance Ratings of Precision Farming Information Sources Table 15. Cotton Yield on Least, Average, and Most Productive Areas of a Typical Field Table 16. Use of Irrigation by Precision Farming Technology User and Non-‐ User Table 17. Devices Used for Farm Management Table 18. Characteristics of Precision Farming technology Users and Non-‐ users Table 19. Final Education Levels Completed by Respondents Table 20. Household Income of Precision Farming Adopters and Non-‐Adopters by Category

9 11 13 16 17 19 20 23 25 25 26 27 28 29 31 32 33 34 34 35

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P R E C I S I O N F A R M I N G B Y C O T T O N P R O D U C E R S I N F O U R T E E N S O U T H E R N S T A T E S R E S U L T S F R O M T H E 2 0 1 3 S O U T H E R N C O T T O N F A R M S U R V E Y

INTRODUCTION

Crop production costs continue to increase. The increased costs of equipment, fuel, fertilizer, and labor have created economic challenges for agricultural producers (Mckinion et al., 2001). Precision farming can provide a way for many crop producers to use inputs more efficiently to reduce costs and/or increase yields. Heterogeneous soil and other factors within a typical agricultural field offer producers opportunities to move away from uniform-field management towards site-specific management through precision farming. Precision farming allows producers to use within-field, site-specific information about soil and plant input requirements to apply the right amount of input in the right place at the right time (Bongiovanni and Lowenber-Deboer, 2004). Precision farming technologies have been developed to identify variability within a field and provide site-specific input applications that match varying crop and soil needs (Cochran et al., 2006; Roberts et al., 2004). These technologies include spatial information technologies such as global position systems (GPS), geographic information systems (GIS), yield-monitor sensors and geo-referenced soil sampling, among others, along with

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computer controlled within-field variable rate input application (VRA) equipment (Bongiovanni and Lowenber-Deboer, 2004). Studies to investigate current use and future prospects for precision farming technologies are important to agricultural producers because findings from those studies provide valuable information for farmers to make adoption decisions. University and industry personnel or crop consultants may also benefit by using those findings to develop effective outreach materials and provide more accurate information to help farmers make decisions. Information on precision farming technologies is particularly important to cotton producers. Cotton is a high valued crop that requires a significant level of input application (Roberts et al., 2013). The average per-acre value of cotton in the United States from the 2012 Census of Agriculture (USDA, 2014) was higher than corn, soybeans, or wheat. The high value and costs of cotton production provide ample incentives for precision farming adoption. In the United States, cotton was harvested on 9.4 million acres or nearly 3 percent of the harvested cropland and is a significant crop throughout the southeast (Figure 1). In Alabama, Georgia, Mississippi, North Carolina, South Carolina, and Texas cotton exceeds 10 percent of total harvested cropland (USDA, 2010). In some Texas counties, more than 50% of the harvested acres were from cotton (USDA, 2010). Alabama, Arkansas, Arizona, Florida, Georgia, Louisiana, North Carolina, Texas, and Tennessee had counties with 25% to 50% of harvested cropland in cotton. Previous reports written from surveys conducted in 2001, 2005, and 2009 indicated an increasing trend in precision farming adoption from 2001 to 2009. In 2001, the survey was conducted in six states—Alabama, Florida, Georgia, Mississippi, North Carolina, and Tennessee

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(Roberts et al., 2002). In 2005, the number of states increased to 11 by adding cotton producers in Arkansas, Louisiana, Missouri, South Carolina, and Virginia (Cochran et al., 2006). In 2009, the study area increased to 12 states by adding Texas (Mooney et al., 2010a).

Figure 1. Harvested Cotton Acres by County in the Southern Production Region and the Proportion of Acres in Cotton, by County, 2012. (Source: USDA, 2014)

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The objectives of these surveys were to identify the current status and trends in the adoption of numerous precision farming technologies by southern U.S. cotton producers. Roberts et al. (2002) identified 23% of responding cotton producers as precision technology adopters. Precision farming adopters were defined as respondents who had used information gathering technologies (e.g., grid soil sampling, yield monitoring) or made variable rate management decisions (e.g., variable rate nitrogen, growth regulator application). The most widely used precision farming technologies were grid and zone soil sampling; variable rate application of lime, phosphorous, and potassium; and soil survey maps. Only 2% of responding producers (28 of 1,373) used yield monitoring with GPS to gather information about within-field yield variability. Cochran et al. (2006) reported that 48% of cotton producers responding to the 2005 Southern Cotton Precision Farming Survey had used one or more precision farming technologies. The 2005 survey used an identical definition of a precision farming adopter to the one used for the 2001 survey. Grid and zone soil sampling and variable rate application of lime, phosphorous, and potassium remained the most commonly used precision farming technologies. However, the use of cotton yield monitoring systems equipped with GPS by respondents grew six percent from 2001. In 2009, 63% of the respondents were identified as precision farming adopters (Mooney et al., 2010a). Zone and grid soil sampling were the most frequently used information gathering technologies, followed by yield monitoring with GPS and soil survey maps. Respondents who used variable rate management did so most frequently with fertility/lime inputs. Growth regulators and harvest aids were commonly applied at variable rates based on information from aerial and satellite imagery. Spraying, planting, and tillage were the most commonly reported

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field operations where GPS guidance was used. Cotton producers consulted information sources such as university extension, researchers, and other farmers to make decisions about precision farming adoption and use. This report summarizes responses to individual survey questions and provides descriptive statistics of precision farming adoption by cotton producers during the 2011 and 2012 production years across 14 southern states—Alabama, Arkansas, Florida, Georgia, Kansas, Louisiana, Mississippi, Missouri, North Carolina, Oklahoma, South Carolina, Tennessee, Texas and Virginia. The 2013 Southern Cotton Farm Survey was designed to continue tracking the adoption of precision technologies on southern cotton farms. Observations from farmers concerning the current use and trends of precision farming are tabulated. As more and more information becomes available, cotton producers are more likely to make better-informed decisions regarding the use of precision technologies. Findings from previous cotton precision farming surveys and the 2013 survey reviewed in this report provide information to help cotton producer make decisions about the adoption and continued use of these precision farming technologies (Mooney et al. 2010a). As adoption of precision farming continues, existing technologies improve, and new technologies emerge, cotton producers will face an expanded array of opportunities for augmenting their production practices. University extension and industry personnel may benefit by using the survey results and subsequent analyses of the responses to develop more effective outreach materials and presentations.

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METHODS SURVEY The 2013 Southern Cotton Farm Survey asked cotton producers about their perceptions and use of precision farming technologies (Appendix I). The questionnaire was pre-tested in July 2012 at the University of Tennessee Milan No-Till Field Day. Information from the pre-test was used to modify the survey instrument. A mailing list of 13,838 potential cotton producers residing within the 14-state survey region for the 2011 marketing year was provided by the Cotton Board (Figure 2). After removing 272 duplicate addresses and the addresses of research and education centers, a list of 13,566 cotton producers remained. Following Dillman’s (1978) mail survey procedures and modifying them slightly, each producer received an initial postcard mailed out January 18, 2013, that informed them they would

Figure 2. Study Area for the 2013 Southern Cotton Farm Survey

be receiving a mail survey about precision farming technologies in two weeks. The survey questionnaire was mailed for the first time February 1, 2013, with a postage-paid return envelope and a cover letter explaining the purpose of the survey. Non-respondents to the questionnaire were sent a reminder postcard on February 8 and, on February 22, 2013, those who had still not responded were sent the | P a g e 6

questionnaire for the second time with a revised cover letter. Figure 3 illustrates the mail survey procedures and indicates the response rate by date. 16%

Response Rate

13.68%

14%

12%

Response Rate

10%

2nd Survey

8%

1st Survey Postcard Reminder

6%

4%

1st Postcard

2%

0%

12/7

1/26

3/17

5/6

6/25

8/14

Figure 3. Cumulative Survey Response Rate for the 2013 Southern Cotton Farm Survey Of the 13,566 questionnaires mailed, 66 were returned undeliverable due to incorrect addresses, 75 individuals declined to participate, and 263 indicated that they (or the addressee) had retired, did not farm cotton, or were deceased. A total of 1,811 usable responses were received by July 15, 2013. The survey response rate of 13.68% for the 14-state region was calculated as the number of usable responses (1,811) divided by 13,237, which was the number of mailed questionnaires (13,566) minus those that were undeliverable (66), and those who had retired, were deceased, or did not grow cotton (263). The 75 who declined to participate were not excluded from the denominator in calculating the response rate since no reason for their refusal was provided (American Association for Public Opinion Research, 2011).

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TECHNIQUES USED TO EVALUATE SURVEY DATA Statistical analysis was conducted for responses to each question using STATA (StataCorp, 2013). This analysis typically focused on the number of observations (N), mean, standard deviation, maximum value, and minimum value. For questions on the use of precision farming technologies, the adoption rates were calculated in excel and compared among the technologies. Adoption rates were obtained by dividing the number of farmers using a technology by the number of survey responses (1,811). Percentages of respondents answering parts of a question were calculated by dividing the number of observations for a category by the number of total observations for the question.

RESULTS Results are presented in five sections. The first section compares age and farm-size characteristics of survey respondents with the 2012 Census of Agriculture (USDA, 2014) and examines farm size and cotton acres of the respondents. The second section presents precision farming technology user rates for selected information gathering, variable rate management, GPS guidance, and automatic section control technologies. Next, technology user responses are profiled for questions about cotton yield monitors, GPS guidance systems, GPS-referenced soil samples, variable rate management, precision farming services, factors influencing adoption, and fiber and environmental quality changes following precision farming adoption. The fourth section discusses respondents’ observations and opinions regarding in field yield variability, precision farming information sources, and the future of precision farming. The final section compares demographic and farm characteristics of precision farming technology users with nonusers.

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COMPARISON OF 2013 SURVEY DATA WITH THE 2012 AG CENSUS Table 1 presents the response rates, numbers of respondents, and distribution of cotton farmers surveyed by state in the 2013 Southern Cotton Farm Survey compared to the 2012 Ag Census (USDA, 2014). The size distribution of cotton farmers surveyed in 2013 is similar to that of Ag Census for most states. Texas is underrepresented and Arkansas is over represented when comparing the respondent location to the 2012 Census (USDA, 2014).

Table 1. Response Rate for the 2013 Southern Cotton Farm Survey and Number of Cotton Farmers Surveyed for the 2013 Survey Compared with 2012 Ag Census State

2013 Survey Responses Nb

Response Rate

2013 Cotton Farmers Surveyed a N

percent

Distribution

2012 Census of Agriculture N

percent

Distribution percent

AL

129

17.1

756

5.7

925

5.6

AR

43

7.1

609

4.6

388

2.3

FL

28

14.0

200

1.5

339

2.0

GA

217

8.8

2466

18.6

2616

15.7

KS

28

15.6

179

1.4

153

0.9

LA

72

15.4

469

3.5

467

2.8

MO

48

11.8

406

3.1

407

2.4

MS

113

18.1

624

4.7

822

4.9

NC

261

19.8

1319

10.0

1430

8.6

OK

33

11.3

292

2.2

451

2.7

SC

88

16.1

545

4.1

783

4.7

TN

117

20.5

571

4.3

546

3.3

TX

598

13.0

4593

34.7

7025

42.3

VA

36

17.3

208

1.6

265

1.6

Total 1811 13.68 13237 100 16617 100 The mailing list of 13,838 potential cotton producers for the 2011 marketing year provided by the Cotton Board was reduced by subtracting 272 duplicate addresses and addresses of research and education centers, 66 addresses that were returned undeliverable, and 263 addresses of those who had retired, did not farm cotton, or were deceased. b N is the number of responses. a

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Figure 4 illustrates the geographical distribution of survey responses by county from the 2013 Southern Cotton Farm Survey and Figure 5 presents the distribution from the 2012 Census of Agriculture (USDA, 2014). The geographic patterns are similar for 2013

Figure 5. Geographical Distribution of the Number of Cotton Producers in the 2012 Census of Agriculture in Southern U.S. survey respondents and the Ag Census, both Figure 4. Spatial Distribution of Survey Respondents for the 2013 Southern Cotton Precision Farming Survey

clustering in four distinct regions. The first region follows the coastal plains from Virginia to Georgia, extending into parts of Florida and

Southern Alabama. The second region is centered along the Mississippi River from central Louisiana to southeastern Missouri and spreads east into west Tennessee and into northern Mississippi and Alabama. The third region is concentrated around the Texas high plains and the forth region is clustered around southeast Texas. The similar patterns observed in the two figures suggest that survey respondents from the 2013 survey well reflect to the geographical crosssection of cotton producers. The majority of cotton farmers ranged in age from 45 to 65 in both the 2013 survey (54.2%) and the 2012 Ag Census (52.1 %) (USDA, 2014) (Figure 6). The proportion of | P a g e 10

respondents over the age of 65 was Comparison of Age Distribution

somewhat higher in the survey (26.9%) than 26.9% 25.9%

65 and over

the Ag Census (25.9%). Cotton farmers younger than 45 years of age represented a

33.5% 29.4%

55 to 64 Age (Years)

smaller share of producers in the survey (18.9%) than in the Ag Census (24.4%).

20.7% 22.7%

45 to 54 11.7% 12.3%

35 to 44

These findings suggest that survey

6.4% 8.8%

25 to 34

respondents were concentrated somewhat

0%

(81.1%) than was found in the Ag Census

2012 Ag Census

0.7% 3.3%

Under 25

more in the middle to upper age groups

2013 Cotton Survey

10%

20%

30%

40%

Figure 6. Comparison of Age Distribution in the 2012 Census of Agriculture in Southern US to the 2013 Southern Cotton Precision Agriculture Survey

(78.1%). The overall mean age was 56.5 years for cotton farmers responding to the survey, compared with the mean age of 55.1

Source: (USDA, 2014)

years reported in the 2012 Ag Census. Table 2. Planted Acres for Cotton Production for the 2013 Survey Compared with 2012 Ag Census Range of Cotton Acres per Farm:

1 to 99

100 to 249

250 to 499

500 or more

151b

221

242

597

Average Cotton Acres per Farm

2013 Survey Na % 2012 Ag Census

12.5

18.2

20

693 acres

49.3

N

882

1,748

1,938

12,374

%

5.2

10.3

11.4

73

529 acres

a

N is the number of responses. If planted cotton acres were reported in both years of 2011 and 2012, the year having the greatest acreage was used to categorize the farm. Source: (USDA, 2014) b

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Producers with 500 or more cotton acres represented a smaller percentage of respondents in the survey (49.3%) than in the Ag Census (73.0%) (USDA, 2014) (Table 2). Conversely, farmers with less than 250 acres were a larger share in the survey (30.7%) than in the Ag Census (15.5%). The percentage of farmers with cotton acres between 250 and 500 acres in the survey (20.0%) was larger than in the Ag Census (11.4%). These results indicate that survey respondents are less concentrated towards larger cotton farms (≥500 acres) and more concentrated towards smaller farms (≤249 acres) than in the Ag Census. The mean cotton acreage of survey respondents is larger than the mean acreage of cotton farmers enumerated in the Ag Census. This difference in acreage means suggests that survey respondents in the larger category (≥500 acres) had larger cotton acreages on average than did farmers in the same Ag Census category. Land resources used to produce cotton and other crops are summarized in Table 3 for the 2011 and 2012 growing seasons (Question 13). The average areas of owned land for cotton production were 226 and 213 acres in 2011 and 2012, respectively. The average areas of rented land for cotton production were 476 and 480 acres in 2011 and 2012, respectively. Thus, the average area rented was more than twice the area owned for cotton production in both 2011 and 2012. The ratio of average area owned to average area rented for dryland picked cotton in 2012 was 0.39 (153/390), and the ratio of owned to rented area was 0.51 (162/315) for dryland stripped cotton. The same ratios for irrigated picked and stripped cotton in 2012 were 0.61 (193/316) and 0.65 (181/279), respectively. The differences between these ratios suggest 1) respondents producing picked cotton managed more rented land relative to owned land than respondents producing stripped cotton, and 2) respondents producing irrigated cotton managed more owned land relative to rented land than respondents producing dryland cotton. | P a g e 12

Average cotton yields in 2011 and 2012 (Question 13) were 868 lb/acre and 1,019 lb/acre, respectively. Average yields from irrigated picked cotton were 1.44 and 1.24 times higher than the average yields for dryland picked cotton in 2011 and 2012, respectively, and average yields for irrigated stripped cotton were 2.89 and 2.49 times higher than dryland stripped cotton, respectively. This difference is largely a result of where stripper cotton is located relative to picker cotton (Figure 7). Table 3. Planted Acres and Yield for Cotton Production Cotton Planted Area and Yield

1180 964 354 399 407 1175 959 354 397 405

2011 Mean Std.Dev acres 226 404 149 284 123 242 192 432 177 305 476 692 384 545 283 538 314 595 256 424

1057 847 358 364 353 1056 848 359 367 354

2012 Mean Std.Dev acres 213 372 153 316 162 562 193 414 181 370 480 701 390 524 315 523 316 613 279 447

304 934 218 360 319

868 783 266 1129 769

362 265 330 327 475

261 838 269 312 307

1019 996 361 1233 899

309 274 324 275 395

374 470

438 757

919 1137

339 439

400 753

773 1021

N Owned (Total) Dryland Picked Dryland Stripped Irrigated Picked Irrigated Stripped Rented (Total) Dryland Picked Dryland Stripped Irrigated Picked Irrigated Stripped Yield (lbs lint/acre) Average Yield Dryland Picked Dryland Stripped Irrigated Picked Irrigated Stripped Other Crops Planted Acres Owned (Total) Rented (Total) a

a

N

N is the number of responses.

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Picker Cotton Yield Ratio (Irrigated/Dry)

Stripper Cotton Yield Ratio (Irrigated/Dry)

Figure 7. Irrigated-to-Dry Yield Ratio for the 2012 Cotton Growers Responding to Question 13.

ADOPTION OF PRECISION FARMING TECHNOLOGIES The Merriam-Webster dictionary (2015) defines adoption as “the act or process of beginning to use something new or different”. There are several methods used to measure this. Griliches (1957) examined rates of use of hybrid corn, in which a set of logistic growth functions were estimated to determine the process of adopting and distributing an invention. The previous survey reports defined precision farming adoption variable as the number of individuals using the technology over total respondents. In this report, adoption will be used only when time is incorporated. This report will refer to those that use precision farming technology as users and those that do not as non-users.

OVERALL PRECISION FARMING TECHNOLOGY USE Slightly over 73% were users of precision farming technologies in the 2013 Cotton Farmer Survey. This was approximately 10% higher than the value reported in the 2009 survey (62.7%) (Table 4). The highest state estimates of adoption found in the 2013 survey data were for Missouri (91.7%), Kansas (89.3%), Arkansas (88.4%), Florida (85.7%), Louisiana (84.7%),

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and Tennessee (82.1%). The precision farming technology user values for the remaining states ranged between 60% and 82% of the respondents from those states. The states with the highest levels of precision farming technology use per respondent in the 2009 survey were Virginia (82.6%), Missouri (82.4%), Tennessee (75.2%), Florida (70.4%), and Mississippi (70.3%). The remaining states included in the 2009 survey had values between 50% and 60%. Precision farming technology users in the 2013 survey were higher for most states than that in the 2009 survey, with the exception of Virginia and South Carolina.

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Table 4. A Comparison between the Numbers of Precision Farming Technology Users by State for 2013 and 2009 surveys 2013 Southern Cotton Farm 2009 Southern Cotton Precision Survey Farming Survey Precision Precision State Farming Proportion a Farming Proportion b Nc (%) N (%) d AL 80 62.0 64 60.4c AR FL GA KS LA MO MS NC OK SC TN TX VA Overall a

38 24 140 25 61 44 85 185 24 57 96 448 23 1331

88.4 85.7 64.5 89.3 84.7 91.7 75.2 70.9 72.7 64.8 82.1 74.9 63.9 73.5

44 19 104 N/A 49 28 90 113 N/A 33 79 419 19 1061

69.8 70.4 61.5 N/A 69.0 82.4 70.3 66.9 N/A 68.8 75.2 55.9 82.6 62.7

Overall precision farming adoption for the 2013 survey includes respondents who checked yes to Question 17 and provided an answer to at least one of Questions 21, 26, 27 and 30 (Appendix I).

b

Overall precision farming adoption for the 2009 survey includes respondents who used an

Information gathering technology, made a variable rate management decision, or used GPS guidance. N is the number of responses. d Adoption rate equals the number of respondents classified as adoption divided by the number of survey responses in that state as reported in Table 1. c

A cotton producer was classified as a precision farming adopter if they indicated using one or more of the following: 1. 2. 3. 4.

Precision farming in general (Question 17), GPS guidance systems (Question 21), Information gathering technologies or automatic section control (Question 26), Variable rate input technologies (Questions 27 and 30).

were used in the production of cotton. In the 2013 survey, 718 respondents indicated they had adopted precision farming in general (Question 17) and 1,214 respondents indicated they had adopted a GPS guidance system (Questions 21 and 26). | P a g e 16

The technologies that represent precision farming in the survey include information on gathering technologies, GPS guidance systems, variable rate management of inputs, and automatic section control for planters or sprayers (Table 5). The category with the highest percentage of users was GPS guidance systems (67.0%). The second highest percentage of users was for information gathering technologies (40.9%), followed by respondents who adopted automatic section control (29.3%) and variable rate input management (25.3%) technologies. The most common combinations of these categories included the use of information Table 5. Adoption Rates by Precision Farming Technology Category Precision Farming Technology Categories

Na

Proportionb

Four Major Categories: Information Gathering (Question 26)

740

40.9

GPS Guidance (Questions 21 and 26)

1214

67.0

458

25.3

531

29.3

658

36.3

397

21.9

415

22.9

398

22.0

523

28.9

359

19.8

246

13.6

Variable Rate Management (Question 30) Automatic Section Control for Planters or Sprayers (Question 26) Combinations of the Four Major Categories: Information Gathering and GPS Guidance (Questions 26 and 21) Information Gathering and Variable Rate Management (Questions 26 and 30) Information Gathering and Automatic Section Control (Questions 26 and 30) GPS Guidance and Variable Rate Management (Questions 21, 26, and 30) GPS Guidance and Automatic Section Control (Questions 21 and 26) Information Gathering, GPS Guidance, and Variable Rate Management (Questions 26, 21, and 30) Information Gathering, GPS Guidance, Automatic Section Control, and Variable Rate Management (Questions 26, 21, and 30) a N is the number of responses. b

percent

Proportion refers to the number of responses for each category divided by 1,811 responses.

gathering technologies and GPS guidance (36.3%), followed by GPS guidance and automatic section control (28.9%), information gathering and automatic section control technologies (22.9%), GPS guidance and variable rate management (22.0%), and information gathering | P a g e 17

technologies and variable rate management (21.9%). Those who reported using precision farming technologies from all four broad categories constituted 13.6% of respondents.

USE OF INFORMATION GATHERING TECHNOLOGIES The information gathering technologies included in Question 26 were yield monitor-with GPS, geo-referenced soil sampling-grid, geo-referenced soil sampling-zone, aerial photos, satellite images, soil survey maps, handheld GPS/PDA, COTMAN plant mapping, electrical conductivity, and digitized mapping. Respondents indicated which of these technologies they had used and the year they began using them, the number of acres on which they were used, and whether they stopped using the technologies and the year when they stopped (Table 6).1 Table 6 contains several columns. The second column is the number of respondents that indicated they used a particular information gathering technology. The next column was calculated based on the 2nd column showing the importance of the technology to those that have adopted information gathering technologies. The third column estimates the proportion of farmers using a particular precision farming technology that grow cotton or column 2 divided by the total number of respondents. Grid soil sampling was adopted most frequently, and on average grid soil sampling was used on 1,325 cotton acres per respondent using the technology. Satellite imagery was the information gathering technology used to manage the most per respondent cotton acreage (1765 acres/respondent). For each information gathering technology, a small percentage of the precision farming users had stopped using the technology, indicating most producers continue to use the information gathering technologies they adopted. 1 In some cases, respondents reported acres managed for other farmers plus acres managed on their own

farms. These responses were reduced to reflect only acres owned plus acres rented by the respondent.

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Table 6. Use of Information Gathering Technologies by Cotton Farmers Average Year Farmers Started Using Technology

Precision Farming Technology Usersa

Information Gathering Technology

N

Proportionb of those that use PF Technologies Percent

Adoption Valuec Percent

Average Number of Acres Per Farm

N

Year

N

Area Acres

Yield monitor-with GPS

366

47.0

20.2

348

2007

300

1,838

Geo-referenced soil sampling-grid Geo-referenced soil sampling-zone Aerial photos

403

54.5

22.3

393

2007

329

1,325

228

30.8

12.6

218

2005

184

1,356

213

28.8

11.8

206

1996

155

1,576

Satellite images

113

15.3

6.2

107

2006

84

1,765

Soil survey maps

239

32.3

13.2

234

1997

177

1,756

Handheld GPS/PDA

148

20.0

8.2

141

2005

114

1,569

COTMAN plant mapping

32

4.3

1.8

29

2001

26

1,699

Electrical conductivity

83

11.2

4.6

83

2008

73

1,115

40

5.4

2.2

39

2007

28

1,854

40.9

731

Digitized mapping Overall Adoptiond

740

626

a

Information gathering technologies include yield monitor-with GPS through digitized mapping in Question 26. Adopters of each specific technology include respondents who completed columns 2, 3, and/or 4 of Question 26. b The values reported in this column refer to the percentage of information gathering technology adopters who used a specific technology (e.g., 366/740=47.0%). They do not reflect overall adoption. c Adoption value refers to the number of responses for each technology divided by 1,811 responses, expressed as a percentage. d Overall adopters are respondents who adopted any one or more of the information gathering technologies.

Respondents used yield monitors with GPS on 634,000 acres by 2013. Twenty percent of the respondents indicated that yield monitors with GPS had been adopted by 2013. Six percent of respondents had adopted satellite imagery by 2013 and used this technology on a little more than 230,000 cotton acres. Twelve percent of respondents used aerial photography on 286,000 acres by 2013. More than 300,000 acres were managed using soil survey maps by 13% of the respondents who reported using this technology (Appendix II, Figure 1). Nearly 22% of the respondents stated they used grid soil sampling on a total of 434,794 cotton acres and 13% indicated they used zone soil sampling on 290,775 acres of cotton by 2013(Appendix II, Figure 2).

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USE OF AUTOMATIC SECTION CONTROL Automatic section control technology was used by 235 respondents for planting (13% adoption rate) and 482 respondents for spraying (27% adoption rate). By 2013, 430,000 cotton acres were planted by respondents using automatic section control and slightly more than one million cotton acres were sprayed using this technology (Figure 8).

USE OF GPS GUIDANCE SYSTEMS Information about respondents who reported using GPS guidance systems is shown in Table 7. More than two thirds of the respondents to Question 21 (68%) reported having used a GPS guidance system. Of those, 91% of the respondents to the question indicated the type of guidance system with more than 62% using Autosteer technology and 18% indicated that they used Lightbar technology. Some guidance users (18%) have operated with both Autosteer and Lightbar technologies. Overall 68% of the producers indicated they have used GPS guidance systems. Table 7. Use of GPS Guidance Systems Question and Answer "Do you use GPS guidance systems?" (Question 21) No Yes "If yes, check the one used"

Na

Proportion

1,731 558 1,173

1,065b

91%

Use Only Lightbar

213

c

18%

Use Only Autosteer

723d

62%

Uses both Lightbar and Autosteer Uses a different system Type of guidance system unknown

91 38 108

8% 3% 9%

a

Number of Responses. Number of respondents to this part of Question 21. Numbers indicating use of Lightbar and Autosteer do not sum to 1,065 because respondents could indicate the use of more than one system; thus, corresponding percentages sum to more than 100%. c Number using Lightbar is the number of respondents checking or specifying use of ONLY Lightbar in Question 21. b

d

Number using Autosteer is the number of respondents checking or specifying use of ONLY Autosteer in Question 21.

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1,200

Automatic Section Control for Sprayers

25%

1,000

20%

800

15%

600

10%

400

5%

200

0%

Thousand Acres

Adoption Rate

30%

0 2008

2009

2010

2011

2012

2013

Newly Observed Total Acres Managed with Automatic Section Control for Sprayers Previously Cumulative Total Acres Managed with Technology Abandonment Acres Removed Cumulative Adoption Rate for Automatic Section Control for Sprayers

Figure 8. Number of Cotton Acres on which Automatic Section Control Technology was Used by Respondents for Planting and Spraying Activities over Time.

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USE OF VARIABLE RATE MANAGEMENT Farmers were asked in Question 27 to indicate whether they had used variable rate technology to apply cotton inputs and they were asked in Question 30 about the inputs they applied using variable rate technology. The variable rate management decisions included use of fertilizers, lime, seed, growth regulators, harvest aids, fungicides, herbicides, insecticides, and irrigation water. Cotton producers could indicate if the variable rate management of an input was implemented using a map-based or sensor-based technology. The map-based method uses information gathering technologies (such as yield monitoring, grid soil sampling, and/or others), along with implicit or explicit yield response functions, to create a variable rate input prescription map that is used by a GPS-based controller on an implement or tractor to guide variable rate application of the input. The sensor-based method employs sensors to measure the site-specific characteristics of a field, and uses that information immediately through a set of decision rules (algorithms) to control a variable rate input applicator on-the-go (Roberts et al. 2004). A total of 459 (25%) respondents indicated they had applied cotton inputs with variable rate technology (Table 8). Fertilizers and lime were the inputs most frequently applied with variable rate technology. Specifically, lime was variable rate applied the most frequently (339 respondents), followed by potassium (332 respondents), phosphorous (322 respondents), and nitrogen (172 respondents). The map-based method was used more frequently than the sensorbased method for all variable rate input management decisions. For lime, potassium, and

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Table 8. Use of Variable Rate Input Management

Year Started Using Variable-Rate

Number of Acres Managed with Variable Rate PerFarm N Mean

MapBased

SensorBased

Year Abandoned

N

N

N

Number of Users a

Nc

Mean

Nitrogen

172

164

2007

150

1,419

97

17

Phosphorous

322

311

2007

283

1,460

201

17

8

Potassium

332

324

2007

292

1,421

213

18

10

Lime

339

329

2007

292

1,500

211

17

8

Seed

76

72

2006

58

2,066

38

7

2

Growth Regulator

80

76

2003

59

1,184

31

10

7

Harvest Aid

37

34

2000

26

1,374

15

5

1

Fungicide

20

18

1999

13

1,304

5

2

0

Insecticide

34

34

1998

26

1,224

11

5

2

Herbicide

37

36

1999

29

1,154

11

5

2

Irrigation

27

24

2001

19

1,043

8

2

0

4

2

2007

3

449

1

Input Applied

Other

10

0

Overall Useb 459 Overall Use (% of 1811) 25.3% a Adoption of variable rate management for each input includes respondents who filled in one or more of columns 2, 3, or 5 for the input in Question 30. b Overall adoption of variable rate input management includes respondents who checked “yes” to Question 27 or filled in one or more of columns 2, 3, or 5 for one or more inputs in Question 30. C N is the number of responses. phosphorus application, the numbers of respondents using the map-‐based method were approximately 12 times the numbers using the sensor-‐based method. For variable rate nitrogen application, the number of respondents using the map-‐based method was about six times higher than the number of respondents using the sensor-‐based method.

USERS OF PRECISION FARMING TECHNOLOGY RESPONSES REGARDING PRECISION FARMING TECHNOLOGIES REGARDING LINT QUALITY AND ENVIRONMENTAL BENEFITS Precision farming users were asked if they noticed any improvement in cotton lint quality (Question 18) and environmental quality (Question 19). A total of 836 farmers responded to

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Question 18, of which 212 (25%) indicated they had noticed improvements in cotton lint quality, 338 (40%) indicated no noticeable improvements, and 286 (34%) were unsure about improvements in lint quality with the use of precision farming practices. A total of 831 farmers responded to Question 19, of which 270 (32%) respondents had noticed improvements in environmental quality from precision farming, 256 (30%) respondents had noticed no improvements in environment quality, and 305 (36%) were unsure if environmental quality had improved with the use of precision farming. Precision farming users were asked to check the most important reason to practice precision farming technologies among profit, environmental benefits and being at the forefront of technology, and then they were asked to rate each reason on a scale from 1 (not at all important) to 5 (extremely important) the importance of each reason (Question 20). Table 9 shows that 90 of respondents believed profit was the most important reason to practice precision farming and only 2 percent believed environmental benefits was the most important reason followed by 7 percent for being at the forefront of technology. The average importance ratings were 4.3 for profit, followed by 3.3 for environmental benefits, and 2.8 for being on the forefront of technology.

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Table 9. Importance Rating of Reasons to Practice Precision Farming

Primary Reason for Using Precision Farming Profit Environmental benefits Be at the forefront of technology a b

Percentage of Respondents Indicating Most Important Reason (N=745) 90.2% 2.4% 7.2%

Importance Ratings from 1 to 5a Nb Mean Std. Dev. 689 4.3 0.9 527 3.3 1.0 530 2.8 1.3

Importance ratings: 1 = Not at all; 2 = Somewhat; 3 = Moderate; 4 = Very; and 5 = Extremely. The number of respondents who rated each reason.

REGARDING VARIABLE-‐RATE MANAGEMENT Variable-rate input management users were asked about who generates the information and maps used for variable rate application (Question 28) and perceptions regarding changes in yield and input use after adopting variable rate input management (Questions 29 and 31). Most respondents used consultants to generate variable rate input application information and maps, followed by dealers, and themselves or family members. Slightly less than half (214 respondents) of respondents (444 total respondents) to Question 29 reported observing a yield increase from using variable rate input management. The average increase in yield was 135 lbs/acre for the 199 respondents reporting the amount of the observed yield increase. By contrast, slightly more than half (227) of the respondents reported no change in cotton yield from using variable rate management (Table 10).

Table 10. Perceptions about the Effect of Variable Rate Input Application on Yield Perception on Yield

Na

My cotton yields increased. My cotton yields increased by: My cotton yields did not change. My cotton yields decreased. My cotton yields decreased by:

214 199 227 3 1

a

Proportion percent 48

Mean Yield Change (lint lb/acre)

51 1

135

500


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After variable rate input management, 20%, 22%, and 22% of respondents observed increases and 37%, 57%, and 57% observed decreases in the amounts of nitrogen, phosphorous, and potassium applied, respectively (Table 11). After variable rate lime application, 14% of respondents observed an increase and 72% observed a decrease in lime application. Of all the inputs reported in Table 11, lime had the smallest percentage of respondents reporting no change in input application and the highest percentage indicating a decrease in input use. Table 11. Input Change after Variable Rate Application Na Input applied with VRT Total Responses Increased Use

Percent Nitrogen

236

N

Percent

N

Percent

Phosphorous 343

N

Percent

Potassium

Lime

350

356

47

20

74

22

76

22

49

14

Decreased Use

88

37

194

57

199

57

258

72

Not Change

101

43

75

22

75

21

49

14

Input applied with VRT

Seed

Growth Regulator

Fungicide

Total Responses

123

Increased Use Decreased Use

24

20

27

23

14

19

18

32

51

41

52

45

18

25

10

18

Not Change

48

39

36

31

41

56

29

51

Input applied with VRT

115

Harvest Aid

Herbicide

73

Insecticide

Irrigation

Total Responses

76

Increased Use Decreased Use

25

33

18

26

11

22

21

28

17

25

12

30

39

33

49

27

Not Change a N is the number of responses.

68

57

Other inputs

50

21 4

19

24

3

14

54

14

67

COTTON FARMER PERCEPTIONS ABOUT PRECISION FARMING PRIMARY BARRIERS TO USING PRECISION FARMING TECHNOLOGY Table 12 summarizes cotton farmers’ perceptions about the primary barrier to using precision farming for users and non-users (Question 23). For users, more than half of the respondents (56%) believed that precision farming was too expensive, followed by uncertain benefits (21%), and continuously evolving technology (19%). Approximately two thirds of the | P a g e 26

non-users believed precision farming was too expensive (62%), followed by uncertain benefits (18%) and precision farming was too complex (9%). Thus, the primary barriers to adoption perceived by cotton producers were the expense of obtaining precision farming technology and its uncertain benefits. Table 12. Primary Barrier to Using Precision Farming Today Primary Barrier

N

Number of Responses Too expensive Benefits uncertain Continuously evolving technology Too complex Too time consuming Not profitable Don’t trust it Too risky a

1241a 636 237 213 160 57 37 30 6

Users Percent 56 21 19 14 5 3 3 1

Non-Users N Percent 408b 254 74 31 37 13 23 16 5

62 18 8 9 3 6 4 1

Numbers of respondents indicating each reason do not sum to 1,241 because respondents could indicate more than one reason. Thus, corresponding percentages sum to more than 100%. b Numbers of respondents indicating each reason do not sum to 408 because respondents could indicate more than one reason. Thus, corresponding percentages sum to more than 100%.

INFORMATION SOURCES Table 13 summarizes the information sources used by respondents to obtain information about precision farming technologies regardless of whether they are or are not using precision farming technologies (Question 24). The primary information sources used by respondents were farm dealers, other farmers, crop consultant, and university extension. When information about precision farming was found on the internet, the primary sources were university extension, news media, and farm equipment dealers.

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Table 13. Precision Farming Information Sources

Information Sources Farm Dealer Crop Consultant University Extension Other Farmers Other Family Trade Show News Media Gov't Agency Number of Respondents a N is the number of responses.

Obtain Information on Precision Farming Na 994 463 439 901 108 358 279 61 1722

Found on the Internet N 133 30 170 64 11 63 169 32 1719

Table 14 reports the ratings respondents attached to the importance of information sources in deciding whether to use or not use precision farming technologies (Question 25). The information sources with the highest average ratings were other farmers (3.4), farm equipment dealers (3.3), crop consultants (3.1), and university extension (3.0), news media (2.0) and government agencies (1.8) were rated the lowest in importance among all information sources.

VARIABLE RATE MANAGEMENT COST-‐SHARE PROGRAMS Cotton farmers were asked if they were aware that cost-share reimbursement programs were available for variable rate nutrient management plans (Question 32) through the Environmental Quality Incentives Program (EQIP) and the Conservation Stewardship Program (CSP) and whether they had ever received cost-share payments from a nutrient management program (Question 33). Thirty percent of respondents reported they were aware of such costshare nutrient management programs before responding to the survey, but only 12% of respondents reported receiving a cost-share payment for having a nutrient management program. | P a g e 28

Table 14. Importance Ratings of Precision Farming Information Sources Rating Information Source Number of Responses 1=Not Important 2=Somewhat Important 3=Moderately Important 4=Very Important 5= Extremely Important Average Rating

N

Percent Farm Dealer 1,186 139 12 179 15 301 25 365 31 202 17 3.3

N Percent Other Family 630 253 40 106 17 96 15 102 16 73 12 2.4

Information Source Number of Responses 1=Not Important 2=Somewhat Important 3=Moderately Important 4=Very Important 5= Extremely Important Average Rating

Crop Consultant 881 166 19 123 14 195 22 271 31 126 14 3.1

Trade Show 786 235 30 189 24 234 30 105 13 23 3 2.4


University Extension 904 143 16 160 18 251 28 246 27 104 12 3.0

News Media 728 303 42 171 23 186 26 52 7 16 2 2.0


Other Farmers 1139 83 7 162 14 290 25 379 33 225 20 3.4

Gov't Agency 603 340 56 124 21 86 14 32 5 21 3 1.8

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DEMOGRAPHIC AND FARM CHARACTERISTICS OF RESPONDENTS FARM CHARACTERISTICS The 2012 total planted cotton acres reported in Question 13 were cross-tabulated with the respondents who reported using the four major categories of precision farming technologies in Table 4. Respondents using a GPS guidance system (1,214) managed the largest number of total planted cotton acres (585,963 acres), followed by respondents (740) using information gathering technologies (370,635 acres), respondents (531) using automatic section control for planters or sprayers (307,095 acres), and those (458) managing inputs with variable rate technology (274,722 acres). On average, precision farming technology users reported higher yields than non-users in all yield categories reported in Table 15 for both dryland and irrigated cotton. Average annual dryland yields for the least, average, and most-productive areas within a typical cotton field (Question 15) were respectively 538, 750, and 996 lb/acre for technology users, and they were 31, 49, and 60 lb/acre lower for non-users, respectively (Table 15). Average irrigated yields for adopters were 787, 1,038, and 1,359 lb/acres for the least, average, and most-productive thirds of a typical field, respectively. The average irrigated yields were 148, 181, and 235 lb/acre, lower for non-users than for technology users. Non-users reported less overall yield variability than adopters, as defined by the range between the most and least productive field areas. The mean range in yields was 458 lbs/acre for dryland users, compared with 429 lbs/acre for dryland non-users, and for irrigated cotton they were 572 lb/acre and 485 lb/acre for technology users and non-users, respectively.

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Table 15. Cotton Yield on Least, Average, and Most Productive Areas of a Typical Field Technology Users N

Yield of Cotton Number of Responses (1,425) Dryland:

Mean

Technology Non-Users

St. Dev.

(lbs. lint/acre)

N

Mean

St. Dev.

(lbs. lint/acre)

Least productive 1/3

913

538

314

271

507

305

Average productive 1/3

916

750

355

272

701

329

Most productive 1/3 Irrigated:

a

a

909

996

434

274

936

415

Least productive 1/3

503

787

321

114

639

401

Average productive 1/3

504

1,038

324

116

857

373

506

1,359

365

115

1,124

444

Most productive 1/3 N is the number of responses.

Table 16 reports average area of irrigated cotton farmed and the type of irrigation systems used (Question 16). If a cotton farmer responded to this question, the respondent irrigated cotton with at least one of the systems listed. The number of precision farming users responding to this question was more than three times the number of non-users. The average area of center-pivot irrigated cotton for precision farming technology non-user was 44% lower than for users. The average areas for furrow and subsurface-drip irrigated cotton were 58% and 52% lower, respectively, for non-users than users. The irrigation systems with the most users among respondents and the largest average irrigated areas for both users and non-users were the centerpivot, flood, and furrow irrigation systems. Questions 2, 3, 6, and 7 asked cotton farmers to report the years when they grew cotton, whether they own livestock, whether they rotated other crops with cotton acreage, and whether they used cover crops on cotton acreage, respectively. Eighty-seven percent of the respondents produced cotton in 2012, 29% owned livestock, and 86% of the respondents rotated other crops with cotton acreage. The respondents who rotated other crops reported rotating approximately

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Table 16. Use of Irrigation by Precision Farming Technology User and Non-User Technology User Irrigation System Furrow Flood Center Pivot Hand Move Solid Set/Fixed Linear Move Big or Traveling Gun Side Roll Subsurface Drip Trickle Total Respondents a

Na 215 17 513 8 2 10 26 5 116 4 753

Mean

Technology Non-Users St. Dev.

(Acres) 432 571 443 570 485 589 123 141 33 25 294 422 81 66 45 37 225 234 46 86

N 44 7 120 4 2 3 2 6 17 0 206

Mean

St. Dev.

(Acres) 183 207 117 111 273 336 76 53 14 8 46 23 45 7 59 49 107 90 0 0


two thirds of their cotton land with other crops. Thirty-five percent of respondents indicated using cover crops on about two thirds of their cotton land. Cotton farmers were asked if they used a computer for farm management (Question 9) and whether they used other electronic devices in the field (Question 10). Fifty-six percent of respondents used a computer for farm management. The other devices most commonly used in the field for farm management were smartphones (40%), laptops (21%), handheld GPS devises (16%), and tablets (15%) (Table 17). The difference between the percentages of technology users (66%) and non-users (28%) using a computer for farm management was striking. Similarly, percentages of adopters and non-adopters using electronic devices in the field ranged between 48% and 15% for smartphones, 27% and 6% for laptop computers, 20% and 5% for handheld GPS devices, and 18% and 5% for tablets. The contrast between users (36%) and non-users (75%) using none of these devices in the field was substantial.

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Table 17. Devices Used for Farm Management

Devices Used Computer Use or Not Yes Electronic Devices Used in the Field Laptop Handheld GPS Smartphone Tablet (e.g., iPad, XOOM, Kindle Fire) None of these a

All Respondents Na % 1,731 972 56 1,782 378 21 291 16 704 40 265 15 822 46

Users N 1,287 846 1,327 352 267 638 243 482

% 66 27 20 48 18 36

Non-Users N % 444 126 28 455 26 6 24 5 66 15 22 5 340 75


OTHER CHARACTERISTICS OF RESPONDENTS Several demographic and other characteristics of precision farming technology users are compared with those of non-users in Table 18. Users of precision farming technologies were: •

six years younger (55 years) than non-user respondents (61 years) (Question 4),

•

had four years less experience making primary farm decisions than non-users (28 versus 32 years) (Question 5),

•

had cellphone coverage on a larger percentage of their fields than non-users (92% versus 82%) (Question 11),

•

had farms about the same distance from the nearest precision equipment dealer (23 versus 24 miles) (Question 12), and

•

relied more heavily on farming for household income than non-users (76% versus 65%) (Question 35).

Table 19 reports education levels of respondents (Question 8). For precision farming adopters, 74% of respondents had attended some college or had earned an Associate’s degree or

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higher. Similarly, 72% of respondents who had not used precision farming had attended some college or had earned an Associate’s degree or higher. A larger percentage of users (34%) had

Table 18. Characteristics of Precision Farming technology Users and Non-users Technology Users Demographic Survey Questions Age (years) Years of Farming Experience Percent of the Fields with Cell Phone Coverage Distance between Farm and Nearest Precision Equipment Dealer Percent of 2011 Income from Farming a

Technology Non-users

Na

Mean

Std. Dev.

N

Mean

Std. Dev.

1316 1313

55 28

13 14

467 461

61 32

13 15

1305

92

22

448

82

36

1270

23

26

417

24

32

1207

76

27

400

65

32


Table 19. Final Education Levels Completed by Respondents Technology Users Education Levels Less Than High School/GED High School/GED Some College Associate’s degree Bachelor’s degree Graduate/Professional Degree Total a

Na

Percent

42 303 444 285 134 111 1,319

3% 23% 34% 22% 10% 8% 100%

Technology NonUsers N Percent

30 96 104 168 34 29 461

7% 21% 23% 36% 7% 6% 100%

All Respondents

N

Percent

72 399 548 453 168 140 1,780

4% 22% 31% 25% 9% 8% 100%


attended some college as their highest level of formal education, compared with 23% of technology non-users, while a smaller percentage of users (22%) had earned an Associate’s degree than non-users (36%). Smaller percentages of adopters earned a high school diploma/GED or less (26%) than non-users (28%), but more users earned a Bachelor’s degree or higher (18%) as their highest level of formal education, compared with non-users (13%).

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Question 34 asked cotton farmers about their 2011 taxable household income. On average, household incomes of precision farming technology users were higher than the incomes of non-users (Table 20). About 40% of users earned household incomes below $100,000, compared with 59% of non-users earning incomes below $100,000. By contrast, 37% of users had household incomes greater than $149,999, whereas 23% of non-users had household incomes above this level. Table 20. Household Income of Precision Farming Adopters and Non-Adopters by Category Technology Users 2011 Taxable Household Income Less than $50,000 $50,000 to $99,999 $100,000 to $149,999 $150,000 to $199,999 $200,000 to $499,999 $500,000 or greater Total Number of Respondents a

Technology Non-Users

All Respondents

Na

Percent

N

Percent

N

Percent

143

12

101

24

244

15

352

28

149

35

501

30

282

23

74

18

356

21

164

13

30

7

194

12

196

16

49

12

245

15

100

8 100

17

4

117

7

420

100

1657

100

1237


CONCLUSION This report characterizes the current status of precision farming technology users who are identified as 2012 southern U.S. cotton producers. Cotton producers are continually confronted with information about the rapidly expanding precision farming industry, but questions about the profitability of these technologies remain (Mooney et al. 2010b). The objective of this study was to determine the status of precision farming technology adoption by cotton producers in 14 southern states. To achieve this objective, a mail survey of 13,566 potential cotton producers was conducted in early 2013.

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In summary, 73.3% of respondents were classified as precision farming adopters (i.e., they reported having used precision farming in general, having used at least one information gathering technology, having applied at least one input at variable rates, having used automatic section control for sprayers or planters, or having used a GPS guidance system). Grid soil sampling was the most prevalent information gathering technology. Nearly two thirds of respondents reported having adopted a GPS guidance system, with most adopters using Autosteer technology. Lime was the input most frequently applied by variable-rate technology, followed by potassium, phosphorous, and nitrogen. Respondents listed their primary sources for obtaining information about precision farming technologies, provided their perceptions about the value and future profitability of precision farming technologies, and answered questions about farm and farm operator characteristics. Future analyses involving the data obtained from this survey will investigate the influence of this information on adoption of precision farming technologies. Precision farming adoption is appealing to farmers because it can decrease input use through tailoring applications to site-specific needs for fertilizer and other chemicals and thus lower production cost and improve profitability. Cotton producers gather information from farm dealer and other farmers along with crop consultant, university extension, and trade show to make decisions about precision farming. Our report of the 2013 survey plus the reports of past surveys can be used by our Working Group and others to hone in on research that will specifically allow farmers to make better decisions about adoption and use of various precision farming technologies. It is the detailed analysis of the trends and data from the survey that will allow us and others (Extension)

| P a g e 36

to make a difference. Conducting this survey was useful as it provided a picture of precision farming technologies in use and the impact of these technologies on farm operations. Presenting the means (etc.) provides a picture of the current status of cotton precision farming, but the big bang will come as the data are analyzed to address specific research questions of interest to farmers, the precision farming industry, Extension, government agencies, and other scientists interested in precision farming. Caution should be used when expanding the information to the population of southern cotton producers. Based on the cotton and total acreage values, the respondents to this survey were larger producers then the average.

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REFERENCES

American Association for Public Opinion Research (AAPOR). 2011. “Standard Definitions.” Available at http://www.aapor.org/AAPORKentico/Communications/AAPORJournals/Standard-Definitions.aspx. Bongiovanni, R. and J. Lowenberg-Deboer. 2004. “Precision Agriculture and Sustainability.” Precision Agriculture 5: 359-387. Cochran, R.L., R.K. Roberts, B.C. English, J.A. Larson, W.R. Goodman, S.R. Larkin, M.C. Marra, S.W. Martin, K.W. Paxton, W.D. Shurley, and J.M. Reeves. 2006. “Precision Farming by Cotton Producers in Eleven States: Results from the 2005 Southern Precision Farming Survey.” Research Report 01-06, Department of Agricultural and Resource Economics, The University of Tennessee, Knoxville, TN. Dillman, D.A. 1978. Mail and Telephone Surveys, the Total Design Method. New York: Wiley & Sons. Griliches, Z. (1957). “Hybrid corn: an exploration in the economics of technological change.” Econometrica 25: 501-522. Mckinion, J. M., J. N. Jenkins, D. Akins, S. B. Turner, J. L. Willers, E. Jallas, F. D. Whisler. 2001. “Analysis of a Precision Agriculture Approach to Cotton Production.” Computers and Electronics in Agriculture 32: 213-228. Mooney, D.F., B.C. English, M. Velandia, J.A. Larson, R.K. Roberts, D.M. Lambert, S.L. Larkin, M.C. Marra, R. Rejesus, S.W. Martin, K.W. Paxton, A. Mishra, E. Segarra, C. Wang, and J.M. Reeves. 2010a. “Precision Farming by Cotton producers in Twelve Southern States: Results from the 2009 Southern Cotton Precision Farming Survey.” Research Report 10-02. Department of Agriculture and Resource Economics, The University of Tennessee, Knoxville, TN. Mooney, D.F., B.C. English, M. Velandia, J.A. Larson, R.K. Roberts, D.M. Lambert, S.L. Larkin, M.C. Marra, R. Rejesus, S.W. Martin, K.W. Paxton, A. Mishra, E. Segarra, C. Wang, and J.M. Reeves. 2010b. “Trends in Cotton Precision Farming: 2000-2008.” In Proceedings of the Beltwide Cotton Conferences, 476-481. Memphis: National Cotton Council of America. Roberts, R.K., B.C. English, J.A. Larson, R.L. Cochran, W.R. Goodman, S.L. Larkin, M.C. Marra, S.W. Martin, W.D. Shurley, and J.M. Reeves. 2002. “Precision Farming by Cotton Producers in Six Southern States: Results from the 2001 Southern Precision Farming Survey.” Research Report 03-02. Department of Agricultural Economics, The University of Tennessee, Knoxville, TN. Roberts, R.K., B.C. English, J.A. Larson, R.L. Cochran, W.R. Goodman, S.L. Larkin, M.C. Marra, S.W. Martin, W.D. Shurley, and J.M. Reeves. 2004. “Adoption of Site-Specific Information and Variable-Rate technologies in Cotton Precision Farming.” Journal of Agricultural and Applied Economics 36: 143-158. | P a g e 38

Roberts, R.K., J.A. Larson, B.C. English, and J.C. Torbett. 2013. “Farmer Perceptions of Precision Agriculture for Fertilizer Management of Cotton,” pp 252-264. In (M. Oliver, T. Bishop, and B. Merchant, eds) Precision Agriculture for Sustainability and Environmental Protection. New York, NY: Earthscan Food and Agriculture, Routlage. StataCorp. 2013. “Stata Statistical Software: Release 13.” StateCorp LP, College Station, TX, U.S. U.S. Department of Agriculture (USDA). 2014. 2012 Census of Agriculture. National Agricultural Statistics Service, U.S. Department of Agriculture, Washington, D.C. U.S. Department of Agriculture (USDA). 2010. Crop Production Summary for 2009. Document CR PR 2-1 (10). National Agricultural Statistics Service, U.S. Department of Agriculture, Washington, D.C. Werriam-Webster, Inc. 2015. “Dictionary.” Available at http://www.merriamwebster.com/dictionary/adoption.

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APPENDIX I: THE QUESTIONNAIRE

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2013 Southern Cotton Farm Survey Please have the farm’s primary decision maker answer the questions. Also, try to provide the best answers that you can. Your answers are confidential and will only be averaged with others to get an accurate picture of cotton farming in 2011 and 2012. Thanks for your time.

Section 1: You and Your Farm

1. Where is most of your farm acreage located? County State 2. In what years did you grow cotton? (Check all that apply) 3.

2008 2010 2012 2009 2011 None [If you have not grown cotton since 2008, please STOP and return the blank survey now.] Do you own livestock? (Check one) Yes No

4.

In what year were you born?

5. 6. 7. 8. 9. 10. 11. 12. 13.

19____

In what year did you start making the primary farm decisions? Do you rotate other crops with your cotton acreage? (Check one) Yes (if yes) Approximately how much cotton land is rotated with other crops? % area → No Do you typically use cover crops on your cotton acreage? (Check one) Yes (if yes) Approximately how much cotton land uses cover crops? % area → No What is your final educational level? (Check one) Less Than High School/GED High School/GED Some College Associate’s degree (including occupational degrees) Bachelor’s degree Graduate/Professional Degree Do you use a computer for farm management? (Check Yes No one) What device(s) do you or your workers use in the field for farm management? (Check all that are used.) Laptop Smartphone Tablet (e.g., iPad, XOOM, Kindle Fire) Handheld GPS None of these What percentage of the fields you manage have cell phone % coverage? Approximately how far from your farm is the nearest precision equipment Miles dealer? On the land you owned or rented from others, how many acres of cotton and other crops did you plant and how productive were those acres in the last two years? Please give your best guess of your average yields (lbs. lint/acre). If you have not grown cotton in the last two years, please skip to question 15. Cotton and Other For 2011 For 2012 Yield/acre Yield/acre Crops Acres Planted Acres Planted (lbs lint/acre) (lbs lint/acre) Owned Rented Owned Rented Cotton (Total) Dryland Picked Lbs lbs Dryland Stripped Lbs lbs Irrigated Picked Lbs lbs Irrigated Stripped Lbs lbs Other Crops | P a g e 41

14.

If you sold cotton from the 2012 crop, what was the average price you received? cents/lb

Please give your best guess for cotton yields (lbs. lint/acre) for the following portions of your typical field the last time you farmed cotton: Most productive 1/3 For Dryland: Least productive 1/3 Average productive 1/3 Most productive 1/3 For Irrigated: Least productive 1/3 Average productive 1/3

15.

16.

Please go to page 2 to answer the next question. On your irrigated cotton fields in the most recent year you grew cotton, how many acres were irrigated under each system listed below? Irrigation System Acres Irrigation System Acres 1. Furrow 6. Linear Move 2. Flood 7. Big or Traveling Gun 3. Center Pivot 8. Side Roll 4. Hand Move 9. Subsurface Drip 5. Solid Set/Fixed 10. Trickle

Section 2: General Questions about Precision Farming Consider the following: “Precision farming” involves collecting information about within-‐field variability in yields and crop needs, and using that information to manage inputs. 17. Have you used precision farming for cotton production? (Check one) Yes No If you answered No, go to Question 21. 18. Have you noticed any improvements in cotton lint quality using precision farming? (Check one) Yes No Don’t know 19. Have you noticed any improvements in environmental quality using precision farming? (Check one) Yes No Don’t know How important were each of the following reasons in your decision to practice precision farming? (Check the 20. most important reason and then rate each reason by circling the appropriate number) Check the Importance most Important Not at all Somewhat Moderate Very Extremely Reason Reason Profit 1 2 3 4 5 Environmental benefits 1 2 3 4 5 Be at the forefront of technology 1 2 3 4 5 21. Do you use GPS guidance systems? (Check one) Yes No If yes, check the one used: Lightbar Autosteer Other 22. Do you think it would be profitable for you to use precision farming in the future? (Check one) Yes No Don’t know 23. In your opinion what is the primary barrier to using precision farming today? (Check one) Don’t trust it Continuously evolving technology Too risky Not profitable Too complex Too expensive Benefits uncertain Too time consuming 24. Please complete the following table about precision farming information sources even if you have not used precision farming technologies. Information Source Farm Crop University Other Other Trade News Gov’t Use Dealer Consultant Extension Farmers Family Show Media Agency a. Mark “X” if the source was used to obtain information about precision farming. | P a g e 42

b. Mark “X” if the source was found on the Internet?

Please go to page 3 to answer the next question. 25. How important have the information sources in the table been in deciding whether to use or not use precision farming technologies? Leave blank for information sources you have not used. Information Source Farm Crop University Other Other Trade News Gov’t Used Dealer Consultant Extension Farmers Family Show Media Agency Place the number for the Importance Ratings: importance rating in the 1 = Not Important, 2 = Somewhat Important, 3 = Moderately Important, box below the information 4 = Very Important, 5 = Extremely Important sources you have used. Place importance rating à If you have not used any precision farming technologies, go to Question 32; otherwise proceed to the next question. 26. For each precision technology listed in Column 1 of the table below, indicate the year you started using it (Column 2) and the number of acres managed with the technology (Column 3). Leave blanks for technologies you never used. If you stopped using a technology, please indicate when and why in Columns 4 and 5. Please refer to the following reasons for stopping in Column 5: A – Not profitable; B – Too complex; C – Too expensive; D – Too time consuming; E – Not worth the time and money; F – Adopted a more advanced Precision Farming Technology; G – Other. Column 1 Column 2 Column 3 If You Stopped Using the Technology: Precision Technology Year Started Number of Acres Column 4 Column 5 Using Managed with Year When You Why Did You Stop? Technology Stopped Using (use codes A-‐G above) Yield monitor – with GPS Geo-‐referenced Soil sampling – grid

Geo-‐referenced Soil sampling – zone

Aerial photos

Satellite images

Soil survey maps

Handheld GPS/PDA

COTMAN plant mapping

Electrical conductivity

Digitized mapping Automatic section control or “auto-‐swath” for planters Automatic section control or “auto-‐swath” for sprayers GPS Auto-‐guidance

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Other (Specify) __________

Section 3: Variable Rate Application on Cotton

27. Have you applied cotton inputs with variable rate technology? Yes No If no, skip to Question 32. 28. Who typically generates the information and maps used to variable-‐rate apply the inputs? (Check one) You or family member Consultant Dealer Other (Specify)___________________ Please turn over to answer the next question. Which s tatement b est r eflects y our p erception o f t he y ield e ffects on your cotton fields from variable rate 29.

input application? Please check the letter. If you check A or C, please indicate your best estimate of the change in yields.

A. My cotton yields increased approximately B. My cotton yields did not change.

lbs. lint/acre.

C. My cotton yields decreased approximately

lbs. lint/acre.

30. For each input you have variable-‐rate applied, indicate the information requested in the columns to the best of your recollection. Leave blanks for inputs you have never variable-‐rate applied. Did You Use a Map-‐ Input Applied Year Started Using Number of Acres Year Stopped Based Variable-‐Rate Managed with Using Or Sensor-‐Based Application for this Variable Rate (leave blank if Tech? Input still i n use) (Put “M” if Map-‐Based, “S” if Sensor-‐Based) Nitrogen Phosphorous Potassium Lime Seed Growth Regulator Harvest Aid Fungicide Insecticide Herbicide Irrigation Other (Specify) _____ 31. Did your input use go up (U), down (D), or not change (NC) after variable-‐rate input application? Answer “U”, “D”, or “NC” in the space provided for each input you have variable-‐rate applied. Answer N/A for inputs you have not variable-‐rate applied. Nitrogen Seed Herbicide Phosphorous Growth Regulator Insecticide Potassium Harvest A id Irrigation Lime Fungicide Other inputs 32. The costs of soil testing and managing fertilizer using variable-‐rate technology may be partially reimbursed under a nutrient management program through the Environmental Quality Incentives Program (EQIP) and the Conservation Stewardship Program (CSP). Were you aware of these cost-‐share reimbursements before this survey? (Check one) Yes No Have you received cost-‐share payments for a nutrient management program? (Check one) Yes 33. No

Section 4: Information about Your Household | P a g e 44

34. Check the range that best reflects your 2011 taxable household income from both farm and non-‐farm sources. Less than $50,000 $150,000 to $199,999 $50,000 to $99,999 $200,000 to $499,999 $100,000 to $149,999 $500,000 or greater About what percentage of your 2011 taxable household income was from 35. % farming? Thank you! Please return the completed survey in the addressed and postage-‐paid envelope.

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APPENDIX II: ADDITIONAL FIGURES

46

Satellite Imagery

6% 4% 2% 0%

250 200 150 100 50 0

Thousand Acres

Adoption Rate

8%

Newly Observed Total Acres using Satellite Images Previously Cumulative Total Acres Managed with technology Abandonment Acres Removed

15% 10% 5% 0%

Aerial Photo

400 300 200 100 0

Thousand Acres

Adoption Rate

Cumulative Adoption Rate

Newly Observed Total Acres Managed with Aerial Photos Previously Cumulative Total Acres Managed with Technology Abandonment Acres Removed

15% 10% 5% 0%

Soil Survey Maps

400 300 200 100 0

Thousand Acres

Adoption Rate

Cumulative Adoption Rate for Aerial Photos

Newly Observed Total Acres Managed with Soil Survey Maps Previously Cumulative Total Acres Managed with Technology Abandonment Acres Removed Cumulative Adoption Rate

Appendix Figure II.1. Adoption of Satellite Imagery, Aerial Photographs, and Soil Survey Maps

47

450 400 350 300 250 200 150 100 50 0

Grid Soil Sampling

20%

Adoption Rate

15% 10% 5% 0%

Thousand Acres

25%

Newly Observed Total Acres Managed with Geo-referenced Soil Sampling-Grid Previously Cumulative Total Acres Managed with Technology Abandonment Acres Removed Cumulative Adoption Rate

14%

Zone Soil Sampling

250

10%

200

8%

150

6%

100

4% 2%

50

0%

0

Thousand Acres

Adoption Rate

12%

300

Newly Observed Acres Managed with Geo-referenced Soil Sampling - Zone Previously Cumulative Total Acres managed with Technology Abandonment Acres Removed Cumulative Adoption Rate

Appendix Figure II.2. Adoption of Geo-referenced Information on Soil Requirements

48