Valuing Agricultural Weather Information Networks Jeffrey D. Mullen, Mohammed Al Hassan, Jennifer Drupple, and Gerrit Hoogenboom

1. Overview  Weather information is an important input in the decision making process for the agricultural sector.  Two types of uses –Reactive management –Proactive management

Reactive Management  Respond to actual weather events –Irrigation decisions  How much to irrigate depends on actual rainfall –Pesticide and fertilizer applications

Proactive Management  Respond to forecasted weather –Pesticide and Fertilizer applications –Harvest timing –Frost protection

 In Georgia, the NWS is the main supplier of short-term weather information - (Hoogenboom et al., 2003), but not always appropriate for ag decisions. 1. The ASOS are located at or near major metropolitan airports. Most airports have a history of site relocations and instrument changes and/or are located within changing urban environments which has degraded the continuity of the long-term data (NWS, 2009) 2. In addition, urbanization and the resulting heat island influence (artificial warming) has made airport data unsuitable for agricultural use.

The Georgia Automated Environmental Monitoring Network (Georgia AEMN)  Due to the problems listed above, the College of Agriculture and Environmental Sciences of the University of Georgia in 1991 established the Georgia AEMN.  The main objective for establishing the Georgia AEMN is to collect reliable weather data and other environmental variables for agriculture and related applications (Hoogenboom, 1993).

Distribution of the Georgia AEMN Stations

2. Problem Statement  Since its establishment in 1991, the Georgia AEMN has produced quality weather products for different applications.  Cut in budgetary allocations to many institutions in recent times, has the potential to affect the operations of existing weather stations and could possibly lead to the termination of some weather stations.  The research question then is, what costs if the Georgia AEMN losses resolution?

Current Studies  Reactive Irrigation Management –Losses in Expected Net Revenue due to suboptimal irrigation actions –In response to less accurate weather information  Proactive Frost Protection –Unnecessary protection costs –Preventable crop losses –In response to less accurate forecasts

3. Irrigation Study Objectives  Develop a methodology that is able to estimate the value of site-specific weather information for irrigated agricultural management.  Application of the methodology to Camilla weather station.

1. To determine, in an expected utility framework, the optimal planting date and irrigation threshold for irrigated corn, cotton, peanut and soybean in Camilla. 2. To simulate average crop yield and estimate expected net revenues for four crops under consideration based on the optimal planting date and irrigation threshold. 3. To estimate the net revenue lost for losing the Camilla Georgia AEMN weather station.

4. Study Area

5. Methodology The methodology is divided into three components. - Agronomic - Economic - Spatial  The agronomic component involves the use of DSSAT to simulate crop yield and irrigation water use at selected locations and on selected soils in the study area.

 threshold  The economic component uses the Constant Relative Risk Aversion (CRRA) utility function to identify the optimal irrigation threshold and planting date for the selected crops.  The spatial component uses kriging and Thiessen polygon analysis in GIS to spatially identify the nearest neighbors of a reference Georgia AEMN weather station (Camilla)

6. Model Specification Step 1: Crop Simulation Crop Management Data for Peanut Production Planting date Peanutcultivar Irrigation thresholds Fertilizer Application Soil type PlantPopulation / m2 Type Amount /Time 04/10 Georgia green 0%,10%,20%,3%,40%,50%,60%,70%,80%,90%Diammoniumphosphate 11kg/ha on 04/10NLSTLS /20 Georgia green 0%,10%,20%,3%,40%,50%,60%,70%,80%,90%Diammoniumphosphate 11kg/ha on 04/20NLSTLS /30 Georgia green 0%,10%,20%,3%,40%,50%,60%,70%,80%,90%Diammoniumphosphate 11kg/ha on 04/30NLSTLS /10 Georgia green 0%,10%,20%,3%,40%,50%,60%,70%,80%,90%Diammoniumphosphate 11kg/ha on 05/10NLSTLS /20 Georgia green 0%,10%,20%,3%,40%,50%,60%,70%,80%,90%Diammoniumphosphate 11kg/ha on 05/20NLSTLS12.9

Crop Management Data for corn Production Planting date Corncultivar Irrigation thresholds Fertilizer Application Soil type PlantPopulation / m2 Type Amount /Time 03/01PIO31G980%,10%,20%,3%,40%,50%,60%,70%,80%,90%Urea 70kg/ha on 03/01 90kg/ha on 03/30 NLSTLS7.2 03/15PIO31G980%,10%,20%,3%,40%,50%,60%,70%,80%,90%Urea 70kg/ha on 03/15 90kg/ha on 04/15 NLSTLS7.2 03/30PIO31G980%,10%,20%,3%,40%,50%,60%,70%,80%,90%Urea 70kg/ha on 03/30 90kg/ha on 04/30 NLSTLS7.2 04/15PIO31G980%,10%,20%,3%,40%,50%,60%,70%,80%,90%Urea 70kg/ha on 04/15 90kg/ha on 05/15 NLSTLS7.2 04/30PIO31G980%,10%,20%,3%,40%,50%,60%,70%,80%,90%Urea 70kg/ha on 04/30 90kg/ha on 05/30 NLSTLS7.2 05/15PIO31G980%,10%,20%,3%,40%,50%,60%,70%,80%,90%Urea 70kg/ha on 05/15 90kg/ha on 06/15 NLSTLS7.2 05/30PIO31G980%,10%,20%,3%,40%,50%,60%,70%,80%,90%Urea 70kg/ha on 05/30 90kg/ha on 06/30 NLSTLS7.2

Planting date soybeancultivar Irrigation thresholds Fertilizer Application Soil type PlantPopulation/m2 Type Amount /Time 05/10 M Group 7 0%,10%,20%,3%,40%,50%,60%,70%,80%,90%DiammoniumPhosphate 70kg/ha on 05/10 NLSTLS20 05/20 M Group 7 0%,10%,20%,3%,40%,50%,60%,70%,80%,90%DiammoniumPhosphate 70kg/ha on 05/20 NLSTLS20 05/30 M Group 7 0%,10%,20%,3%,40%,50%,60%,70%,80%,90%DiammoniumPhosphate 70kg/ha on 06/30 NLSTLS20 06/10 M Group 7 0%,10%,20%,3%,40%,50%,60%,70%,80%,90%DiammoniumPhosphate 70kg/ha on 06/10 NLSTLS20 06/20 M Group 7 0%,10%,20%,3%,40%,50%,60%,70%,80%,90%DiammoniumPhosphate 70kg/ha on 06/20 NLSTLS20 Crop Management Data for Soybean Production

Planting date Cottoncultivar Irrigation thresholds Fertilizer Application Soil type Plant Population/ m2 Type Amount /Time Amount /Time 04/01Delapine5550%,10%,20%,3%,40%,50%,60%,70%,80%,90%AmmoniumNitrate 20kg/ha on 04/01 20kg/ha on 04/24 20kg/ha on 05/24 NLSTLS14 04/15Delapine5550%,10%,20%,3%,40%,50%,60%,70%,80%,90%AmmoniumNitrate 20kg/ha on 04/15 20kg/ha on 05/06 20kg/ha on 06/06 NLSTLS14 04/30Delapine5550%,10%,20%,3%,40%,50%,60%,70%,80%,90%AmmoniumNitrate 20kg/ha on 04/30 20kg/ha on 05/21 20kg/ha on 06/21 NLSTLS14 05/15Delapine5550%,10%,20%,3%,40%,50%,60%,70%,80%,90%AmmoniumNitrate 20kg/ha on 05/15 20kg/ha on 06/05 20kg/ha on 07/05 NLSTLS14 05/30Delapine5550%,10%,20%,3%,40%,50%,60%,70%,80%,90%AmmoniumNitrate 20kg/ha on 05/30 20kg/ha on 06/21 20kg/ha on 07/21 NLSTLS14 Crop Management Data for Cotton Production

Step 2: Determination of Optimal Planting Step 2: Determination of Optimal Planting dates and irrigation thresholds dates and irrigation thresholds NR = TR – TVC ………......……………..………(1) NR = TR – TVC ………......……………..………(1) risk = 1.1 moderate risk aversion risk = 1.1 moderate risk aversion risk = 2.5 significant risk aversion risk = 2.5 significant risk aversion

Step 3: Estimating the Net Revenue Lost for Losing the Camilla AEMN Weather Station Camilla AEMN Weather Station (a) Apply the optimal planting dates and irrigation thresholds identify in step 2 for Camilla to the weather information of Arlington, Attapulgus, Dawson, Tifton, Fort valley, and Plains and simulate for irrigation water use. (discrete irrigation) (b) Apply the discrete irrigation schedule back to Camilla’s weather and simulate crop yield for all four crops and estimate expected NR

(c) Estimate the difference in Expected Net Revenue between step 1 and step 3 (b). This is the measure of expected producer welfare change

Step 3: Continued Step 3: Continued (d) Apply the Thiessen Polygon approach in GIS to determine the closest neighbors of Camilla. (d) Apply the Thiessen Polygon approach in GIS to determine the closest neighbors of Camilla. Thiessen Polygons with All Weather Stations

Thiessen Polygons without the Camilla Station Step 3 (d): Continued

Thiessen Polygons Showing an Overlay of the with and without Camilla station Step 3: (d) Continued

Step 3: Continued  (e). Create an interpolated surface across the study area through kriging (in GIS) using the expected NR difference in step 1 and step 3(b).  (f). Estimate the average value for the sub-Camilla polygons (Camilla A, Camilla B, Camilla C, Camilla D,) through Zonal Statistics in GIS.  (g). Estimate the net revenue lost for losing the Camilla station for each crop by subtracting the average value for the sub-Camilla polygons from the lost expected NR of the corresponding nearest neighbors.

 Step 4: Estimating the value of the Camilla Station a. Estimate the number of irrigated acres of corn, cotton, peanut and soybeans on NLS and TLS within the Camilla polygon. We assume NLS and TLS are evenly distributed within the Camilla polygon 2. Value of the Camilla station is the net revenue lost for losing Camilla for each crops multiplied by the number of irrigated acres on NLS and TLS for each crop

6. Results  Optimal Strategies: CropSoiltypeOptimalIrrigation (%) (%) Optimal planting date Ave. Yield (kg/ha) Expected water used (mm/ha) CottonTLS404/ CottonNLS404/ PeanutTLS705/ PeanutNLS604/ CornTLS405/ CornNLS505/ SoybeanTLS505/ SoybeanNLS505/

 Expected NR lost Weather stations Peanut (NLS) Peanut (TLS) Soybean (NLS) Soybean (TLS) Corn (NLS) Corn (TLS) Cotton (NLS) Cotton (TLS) Arlington Attapulgus Dawson Fort valley Plains Tifton

Summary of Net Revenue LostCrop/soil NR Lost for losing Camilla ($/ha) Corn (NLS) -29 Corn (TLS) -31 Peanut (NLS) -49 Peanut (TLS) -43 Cotton (NLS) -24 Cotton (TLS) -37 Soybean (NLS) -72 Soybean (TLS) -63

 Total NR Lost for losing Camilla (Corn NLS and TLS) CountyProportion of county in Camilla polygon Total irrigated corn hectares on NLS in the Camilla polygon Total irrigated corn hectares on TLS in the Camilla polygon Total NR lost for irrigated corn on NLS Total NR lost for irrigated corn on TLSBaker Calhoun Colquitt Decatur Dougherty Grady Mitchell Thomas Worth Total$-74,482$-140,844

 Total NR Lost for losing Camilla (Cotton NLS and TLS) CountyProportion of county in Camilla polygon Total irrigated cotton hectares on NLS in the Camilla polygon Total irrigated cotton hectares on TLS in the Camilla polygon Total NR lost for irrigated cotton on NLS Total NR lost for irrigated cotton on TLSBaker Calhoun Colquitt Decatur Dougherty Grady Mitchell Thomas Worth Total$-106,908$-184,578

 Total NR Lost for losing Camilla (Peanut NLS and TLS) CountyProportion of county in Camilla polygon Total irrigated peanut hectares on NLS in the Camilla polygon Total irrigated peanut hectares on TLS in the Camilla polygon Total NR lost for irrigated peanut on NLS Total NR lost for irrigated peanut on TLSBaker Calhoun Colquitt Decatur Dougherty Grady Mitchell Thomas Worth Total$-130,668$-146,606

 Total NR Lost for losing Camilla (Soybeans NLS and TLS) CountyProportion of county in Camilla polygon Total irrigated soybean hectares on NLS in the Camilla polygon Total irrigated soybean hectares on TLS in the Camilla polygon Total NR lost for irrigated soybean on NLS Total NR lost for irrigated soybean on TLSBaker Calhoun Colquitt Decatur Dougherty Grady Mitchell Thomas Worth Total$-14,281$-49,143

 Overall NR lost for losing the Camilla station is estimated at $847,502 per year for irrigated corn, cotton, peanut and soybeans.

Frost Forecasting  Determine probability of incorrect forecast –Type A Error: Predict Frost when none –Type B Error: Fail to Predict Frost  Estimate costs of each type of error  Examine how the probability of these errors changes as network resolution changes  Calculate change in Expected Costs