MODELING AGRICULTURAL PRODUCTION AND RESOURCE USE

• Sponsoring Institution: National Institute of Food and Agriculture
• Project Status: COMPLETE
• Funding Source: HATCH
• Reporting Frequency: Annual
• Accession No.: 0216186
• Project Start Date: Oct 1, 2008
• Project End Date: Sep 30, 2013
• Program Code: [(N/A)]- (N/A)

Recipient Organization
UNIVERSITY OF CALIFORNIA, DAVIS
410 MRAK HALL
DAVIS,CA 95616-8671

Performing Department
Agricultural and Resource Economics

Non Technical Summary
California's agricultural industry and the land and water resource base on which it exists are facing increasing scarcity and competition from urban expansion and environmental resource demands. It is also recognized that the natural uncertainty in the water supply, crop prices and growing seasons must be taken into account if models are to accurately predict farmer response to policy changes. This new project extends the general approach of applying production models for policy analysis, we will apply similar modeling methods to the salinity build up problem in the San Joaquin Valley. In addition, potential solutions to the current impasse between fish populations and water exports in the Sacramento Delta will be analyzed. Work on the economics of crop rotations will also allow the measurement of the economic impacts of more sustainable agricultural practices. The models developed and tested in this project will be used as part of an interdisciplinary assessment of current resource use by California agriculture and its ability to adapt to climate change and changing economic conditions. The economics of sustainable agriculture and carbon sequestration in California will also be examined, as will the effect of changing salinity in the San Joaquin valley.

Animal Health Component

(N/A)

Research Effort Categories

Basic

(N/A)

Applied

(N/A)

Developmental

(N/A)

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
601	6110	3010	100%

Knowledge Area
601 - Economics of Agricultural Production and Farm Management;

Subject Of Investigation
6110 - Economy of the United States and sectors thereof;

Field Of Science
3010 - Economics;

Keywords

production modeling

primal-dual models

california agriculture

water use

land use

Goals / Objectives
1. Develop and test methods for improved modeling in agricultural production, resource use, and secondary environmental effects. 2. To integrate the concept of primal/dual estimation into an empirical estimation approach for production models. 3. Show how the expanding set of GIS data on crop choice, soil type, and microclimate can be incorporated into economic models of production. 4. Integrate the production models with other agronomic and engineering models for policy analysis. 5. Development and application of the primal-dual approach to evaluate firms' productivity. rms' productivity.

Project Methods
Two different modeling approaches will be used in this project. The first approach is a primal/dual methodology to econometric estimation that incorporates expectations and uncertainties on both the supply and demand components that drive farmer decisions. The project focuses also on alternative methodological approaches to estimate and evaluate the productivity of California farms. Briefly, these alternative methods can be grouped under the heading of a primal-dual framework that makes a more complete use of the available information. The traditional approach to estimate and evaluate farm productivity has been based exclusively on either a primal framework or a dual viewpoint. This means that, from a primal vista, the estimation procedure involved the production function and the system of first-order conditions. From a dual perspective, instead, the cost function and the system of derived demand functions were estimated. In the proposed approach, the primal and the dual relations will be jointly estimated to account for all the cross-equation restrictions involving both variables and parameters. This procedure ought to give more efficient and reliable estimates. The second approach uses a primal specification that uses maximum entropy and logistic econometric methods to estimate agricultural production models from detailed field-based GIS data. These disaggregated production models form the basis of several different empirical applications of economic policy to reduce the cost of agricultural environmental external effects. Common examples are drainage, dust pollution, water use, salinity accumulation, groundwater contamination. All of these effects are generated by a linked physical and economic process. The physical models that are linked to the economic production processes are defined by a greater level of spatial and temporal detail than normal in economic decision models. An economic model that can inter-face with physical process models must also be specified and estimated on a more disaggregate basis, and consequently with smaller sample sizes. Maximum entropy estimators have the property that they produce consistent estimators even with very low degrees of freedom (Golan et al. 1996). This property enables flexible form production functions such as quadratic or CES specifications to be estimated using small, or sometimes ill posed data sets. When linked with detailed GIS based data on farm production and resource endowments, such disaggregated models can produce bio-economic policy predictions that have a realistic physical and economic response to changes in quantity restrictions or prices. In empirical applications, the agricultural production models will be used to evaluate the use of financial incentives such as options and auctions for water allocation and environmental uses, and a measurement of the economic costs of increased sub-surface salinity from irrigation.

Progress 10/01/08 to 09/30/13

Outputs
Target Audience: The main target audiences of this research project are the community of agricultural economists and the upcoming PhD students in agricultural economics. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? The project has provided the opportunity of training several classes of graduate students within the ARE department and professional agricultural economists in several countries (Italy, Iran, Canada). How have the results been disseminated to communities of interest? The project results have been disseminated in the form of journal articles, a book chapter, and numerous reports that were made available online. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? The project has accomplished goals 1, 2 and 5. Under goal 1, the project has developed a positive mathematical programming model that uses information on realized output levels and prices of limiting inputs such as land, family labor, irrigation water. This approach requires the specification of primal and dual constraints. Under goal 2, the project has developed an econometric method that requires the joint estimation of a production and the associated cost function. This is due to the hypothesis that the adoption of technical progress may be expressed by introducing input and output prices into the production function. This is a novel but fruitful approach that leads to a series of testable hypotheses that identify different types of technical progress. Under goal 5, the project has developed a method for dealing with generalized risk. In other words, to date, risk programming was confined to deal only with a particular type of risk preferences represented by a constant absolute risk parameter. This vintage model relies on the application of a negative exponential expected utility and normality of market output prices. The new methodology, based upon a utily function, specified in terms of expected wealth and its standard deviation, admits any type of risk preferences. This model has been applied in a symmetric positive equilibrium problem (SPEP) context for analyzing the agricultural policies of the Euuropean Union. It requires also the application of the duality of the least squares method.

Publications

Progress 01/01/12 to 12/31/12

Outputs
OUTPUTS: I conducted empirical verification about the correctness of the dual specification of the least squares method. I did similar verification concerning the dual specification of the maximum likelihood approach. PARTICIPANTS: Not relevant to this project. TARGET AUDIENCES: Not relevant to this project. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.

Impacts
Change of knowledge. The dual of the least squares method is a novel structure that produces identical estimates of the parameters of a statistical linear model in an entirely different way from the 200 year old methodology. Instead of minimizing the sum of squares residuals, the dual approach maximizes the net value of sample information. Similar conclusions concern the maximum likelihood approach.

Publications

• No publications reported this period

Progress 01/01/11 to 12/31/11

Outputs
OUTPUTS: I conducted extensive simulations to verify the correctness of the analytical formulation associated with equilibrium problem. PARTICIPANTS: Sophie Drogue, INRA-AgroParisTech, UMR Economie Publique, 16 rue Claude Bernard, 75231 Paris Cedex 05, France. Giovanni Anania, Department of Economics and Statistics, University of Calabria, I-87036, Arcavacata di Rende (CS), Italy. TARGET AUDIENCES: Nothing significant to report during this reporting period. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.

Impacts
Change in knowledge. The equilibrium problem is a novel mathematical programming structure that frees models from the shackles of requiring either a maximizing or a minimizing objective function.

Publications

• Quirino Paris , Sophie Drogue, and Giovanni Anania. 2010. Calibrating Mathematical Programming Spatial Models. AGFoodTrade Working Paper 2009-10 (revised March 2010) http://ageconsearch.umn.edu/bitstream/115429/2/AgFoodTradeWP2009-10_A nania_et_al.pdf
• Quirino Paris, Sophie Drogue, Giovanni Anania. 2011. Calibrating spatial models of trade. Economic Modelling. Vol. 28, Issue 6, pp. 2509-2516.

Progress 01/01/10 to 12/31/10

Outputs
OUTPUTS: A methodology of combining primal and dual specifications for the estimation of production and cost relations was finalized into an overall model. The application of interest has been to the US agriculture with the objective of applying a similar approach to California agriculture. The major result was an estimation of price induced technical progress in US agriculture. A PMP (Positive Mathematical Programming) model for evaluating agricultural policies. This approach allows the assessment of realized decisions by economic agents in response to agricultural subsidies and other policy instruments both at the individual farm level and at the regional level. PARTICIPANTS: Not relevant to this project. TARGET AUDIENCES: The target audience was the US profession of agricultural economists who deal with agricultural forecast and policy analysis. Knowledge of the rate of technical progress is crucial for correct assessment of these activities. PROJECT MODIFICATIONS: Not relevant to this project.

Impacts
I do not have any direct measure of impacts of the above results.

Publications

• Quirino Paris. 2008. Price-induced technical progress in 80 years of US agriculture, Journal of Productivity Analysis, 30:29-51.
• Quirino Paris. 2010. Economic Foundation of Symmetric Programming, Cambridge University Press.

Progress 01/01/09 to 12/31/09

Outputs
OUTPUTS: No report at this time. PARTICIPANTS: Not relevant to this project. TARGET AUDIENCES: Not relevant to this project. PROJECT MODIFICATIONS: Not relevant to this project.

Impacts
No report at this time.

Publications

• No publications reported this period