Projects Funded for Bruce Babcock

2020-2021

Impact of SGMA on Crop Mix in California

Bruce Babcock and Dat Tran

Abstract

Specific Objectives of the Project:
Develop appropriate datasets and modeling framework to be used to estimate SGMA impacts.
Develop the modeling capability to simulate changes in crop mix in the San Joaquin Valley and to carry out the simulations.

Project Report/Summary of Results:
We developed a new approach for estimating supply elasticities for California tree crops. The purpose was to obtain reliable elasticities to populate PMP models of water basins in the San Joaquin Valley. Supply elasticities are estimated by regressing observed lagged acreage changes on estimated demand shifts and then converting the resulting regression coefficients into a price elasticity using the assumption of rational expectations. Estimated demand shifts can be estimated from observed prices and quantities if demand elasticities are known. We estimated possible ranges in demand elasticities from observed, unique, events that shifted demand. The supportable range of annual demand elasticities and the most likely elasticities are shown in Table 1 for important California tree crops.

Table 1. Estimated Elasticities of Demand for Citrus and Tree Nut Crops

CropLikely RangeMost Likely Elasticity
Almonds[-0.15, -0.45]-0.40
Lemons[-0.20, -0.60]-0.40
Mandarins[-0.30, -0.70]-0.50
Fresh Navel Oranges[-0.30, -0.70]-0.50
Pistachios[-0.30, -0.70]-0.50
Walnuts[-0.10, -0.30]-0.20

California acreage and supply elasticity estimates for almonds, mandarins, pistachios, and walnuts are shown in Table 2. Estimated U.S. corn and soybean elasticities are also shown to facilitate a comparison of estimates made using our new approach and estimates made using more traditional approaches.

Table 2. Calculation of Supply Elasticity from Acreage Response and Demand Elasticity (1980–2019)

Crop

Elasticity of
Acreage to Demand

Demand ElasticitySupply Elasticity
Corn0.305-0.35 & -0.250.154 & 0.110
Soybeans         0.281-0.350.137
Almonds0.540-0.400.470
Mandarins0.611-0.500.785
Pistachios0.733-0.501.373
Walnuts0.541-0.200.236

2019-2020

Impact of SGMA on Crop Mix in California

Bruce Babcock and Dat Tran

Abstract

Specific Objectives of the Project:
Develop appropriate datasets and modeling framework to be used to estimate SGMA impacts.
Develop the modeling capability to simulate changes in crop mix in the San Joaquin Valley and to carry out the simulations.

Project Report/Summary of Results:
We developed a new approach for estimating supply elasticities for California tree crops. The purpose was to obtain reliable elasticities to populate PMP models of water basins in the San Joaquin Valley. Supply elasticities are estimated by regressing observed lagged acreage changes on estimated demand shifts and then converting the resulting regression coefficients into a price elasticity using the assumption of rational expectations. Estimated demand shifts can be estimated from observed prices and quantities if demand elasticities are known. We estimated possible ranges in demand elasticities from observed, unique, events that shifted demand. The supportable range of annual demand elasticities and the most likely elasticities are shown in Table 1 for important California tree crops.

Table 1. Estimated Elasticities of Demand for Citrus and Tree Nut Crops

CropLikely RangeMost Likely Elasticity
Almonds[-0.15, -0.45]-0.40
Lemons[-0.20, -0.60]-0.40
Mandarins[-0.30, -0.70]-0.50
Fresh Navel Oranges[-0.30, -0.70]-0.50
Pistachios[-0.30, -0.70]-0.50
Walnuts[-0.10, -0.30]-0.20

California acreage and supply elasticity estimates for almonds, mandarins, pistachios, and walnuts are shown in Table 2. Estimated U.S. corn and soybean elasticities are also shown to facilitate a comparison of estimates made using our new approach and estimates made using more traditional approaches.

Table 2. Calculation of Supply Elasticity from Acreage Response and Demand Elasticity (1980–2019)

Crop

Elasticity of
Acreage to Demand

Demand ElasticitySupply Elasticity
Corn0.305-0.35 & -0.250.154 & 0.110
Soybeans         0.281-0.350.137
Almonds0.540-0.400.470
Mandarins0.611-0.500.785
Pistachios0.733-0.501.373
Walnuts0.541-0.200.236