The impacts of information and biotechnologies on corn nutrient management Jae-hoon Sung and John A. Miranowski Department of Economics Iowa State University 20th ICABR Conference

Yield monitoring Pest scouting Soil testing Source: USDA’s agricultural Resource Management Survey Notes: The estimates regarding percentages of acres are based on weighted sum, where the weights were calibrated so that the sum of planted acres for corn based on the survey data match NASS published estimates of planted corn acreage for each survey year. Information and biotechnology used in corn production More than 30% of corn acreage adopted at least one of information technologies. In 2010, yield monitoring was adopted for many large fields. Pest scouting has been more widely adopted compared to soil testing and yield monitoring. 90% of corn acreage was planted to GM corn in 2013 (Fernandez et al. 2014).

Productivity of chemical inputs Information technologies may induce farmers to use nutrient inputs more efficiently by providing the information regarding efficient crop nutrient use. GM corn may increase the productivity of chemical use by reducing yield loss from pest and improving corn root system development. Increases in the efficiency and productivity of chemical use decrease the amount of cropland and chemicals needed to produce a given crop output. Source: (left) (right)

Research Questions How does adoption of information technology affect farmer’s nitrogen use, yields, and nitrogen use efficiency (NUE)? How does the adoption of GM corns correlate with the impacts of information technology?

Nutrient use Consider nutrient use of a farmer i adopting a practice k. where I t (I r ) means an index representing the farmer's decisions regarding adoptions of yield monitoring, scouting, and/or soil testing. D i is a dummy for GM corn adoption X i includes yield management practices, weather and soil conditions, relative prices of corn to soybeans, and nitrogen prices.

Expected and Counterfactual Nutrient Use The expected nutrient use of the farmer i is as follow

Data The Agricultural Resource Management Survey (ARMS) Phase II and III data from 2001, 2005, and 2010 are used for field-level information: chemical use, yield corn grain, field practices, areas of field, and characteristics of farmer. 2,011 fields for corn grain in 14 states are included. CBOT futures prices for corn and soybeans and NASS nitrogen prices are incorporated for state-level price variables. County-level soil and weather conditions are based on gSSURGO and PRISM data. NUE (lb/bushel)Nitrogen (lb /acre)Yield (bushel/acre)# of obs Soil testing or scouting Yield monitors All Non-adopter Note: The estimates are the simple average values over observations including each groups. ( ) represents the number of observations regarding yield monitoring and weed scouting. Source: USDA's ARMS data in 2001, 2005, and 2010.

Unit: %NUENitrogen useYield Yield monitoring -8.1***-7.1***5.4 Soil testing or scouting-5.9***-5.3*** 4.4** All-7.2***-3.9** 9.4*** The impact of information technologies Information technologies induce farmers to use nitrogen more efficiently. Adopters of information technologies have improved NUE. Information technologies have significant effects on total nitrogen applied. Adopting information technologies significantly increases corn yields.

Units: % NUE (lb of nitrogen/bushel) Nitrogen use (lb/Acre) Yield (bushel/acre) Adopters of soil testing or scouting ** Adopters of yield monitoring **17.23** Adopters of all technologies *** Non-adopters The impacts of information technologies and GM corn The effects of GM corn are inconsistent among farmers. The effects of GM corn on corn yields are significant only when farmers adopted information technologies. GM corn increases nitrogen use of farmers who adopted yield monitoring

Reference slides

Slide Notes 5 NUE (lb of nitrogen/bushel) Nitrogen use (lb/Acre) Yield (bushel/acre) Adopters of yield monitoring and soil testing (or weed scouting) %***-38.9%**94.0%*** Adopters of soil testing and weed scouting %***-52.9%***43.7%** Adopters of yield monitoring %***-70.6%***53.5%

Table 1. Summary statics: Dependent variables, prices, policies, and farm characteristics Summary statistics-NUE and nitrogen use Variable Mean Values (Std.Dev) Definition All Soil testing and scouting Yield monitorsNon-adopter GM corn0.69 (0.46)0.54 (0.50)0.43 (0.50)0.35 (0.48)GM seeds used (1=yes, 0=no) Off-work3.31 (7.56)5.37 (9.08)3.19 (7.42)5.94 (9.36)Off-work hours per year (operator and operators' spouse. 100 hours) Tenure28.90 (12.13)29.00 (12.81)28.29 (12.91)29.70 (13.37)Number of years farmer has operated the field College0.32 (0.47)0.21 (0.41)0.23 (0.42)0.16 (0.37)Farm operator graduated college (1=yes, 0=no) Ownership 0.44 (0.50)0.49 (0.50)0.50 (0.50)0.54 (0.50)Field owned by farm operator (1=yes, 0=no) Field area84.51(68.09)53.03 (44.91)59.50 (42.06)43.06 (41.43)The size of corn field (acre) Total land2247.1(2261.7)1227.7(1621.7)1529.8(1275.6)876.8(1170.1)Total land operated during the survey year (acre) Conservation0.08 (0.27) 0.06 (0.24)0.03 (0.17)Farmer participates in public conservation programs (1=yes, 0=no) Tillage0.56 (0.50)0.49 (0.50)0.45 (0.50)0.37 (0.48)Conservation tillage adopted in field (1=yes, 0=no) Rotation0. 81 (0.39)0.82 (0. 38)0.89 (0.31)0.90 (0.30)Corn alternated with other crops (1=yes, 0=no) Irrigation0.14 (0.35)0.13 (0.33)0.04 (0.20)0.05 (0.21)Field irrigated (1=yes, 0=no) Fall application0.41 (0.49)0.25 (0.44)0.38 (0.49)0.21 (0.41)Nitrogen applied during the previous fall (1=yes, 0=no)

Table 1. Summary statics: Dependent variables, prices, policies, and farm characteristics Summary statistics-continue Variable Mean Values (Std.Dev) Definition BothSoil testingYield monitorsNon-adopter 0.43 (0.04)0.43 (0.03)0.44 (0.03) Corn price/soybean price 0.38 (0.03) 0.40 (0.03) Nitrogen price ($/lb) 1.21 (0.07) 1.18 (0.06) Total cost of HT corn seeds/total cost of conventional seeds ($/approximately 80,000 kernel bag) NCCPI0.52 (0.21)0.51 (0.20)0.54 (0.19)0.53 (0.17)National Commodity Crop Productivity Index-corn and soybeans K-factor0.15 (0.10)0.16 (0.10)0.15 (0.10)0.17 (0.11)K factor Slope2.16 (1.97)2.29 (1.98)2.16 (1.89)2.21 (2.06)Representative slope (%) % Sand10.04 (8.95)11.23 (11.69)9.90 (10.30)9.55 (7.97)Sand percentage in soil layer (0~30 cm) % Silt25.90 (18.35)26.68 (17.74)24.15 (17.03)27.30 (18.79)Silt percentage in soil layer (0~30 cm) Ksat5.33 (5.87)6.11 (8.86)5.11 (6.94)4.99 (5.23)Hydraulic Conductivity (m/second) GDD (251.0) (279.2) (298.5) (335.2)Growing degree days Precipitation (174.1) (178.6) (165.0) (171.2)Total precipitation during the growing seasons

Estimation To correct sample selection, specify equations regarding adoptions of the technologies and assume that error terms in all equations follow multivariate normal distribution. Then Equation (2) becomes

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