ESTIMATING CORN YIELD RESPONSE MODELS TO PREDICT IMPACTS OF CLIMATE CHANGE

Projections of the impacts of climate change on agriculture require flexible and accurate yield response models. Typically, estimated yield response models have used fixed calendar intervals to measure weather variables and omitted observations on solar radiation, an essential determinant of crop yield. A corn yield response model for Illinois crop reporting districts is estimated using field data. Weather variables are time to crop growth stages to allow use of the model if climate change shifts dates of the crop growing season. Solar radiation is included. Results show this model is superior to conventionally specified models in explaining yield variation in Illinois corn.Crop Production/Industries,

Determining Optimal Levels of Nitrogen Fertilizer Using Random Parameter Models

The parameters of yield response functions can vary by year. Past studies usually assume yield functions are nstochastic ‘‘limited’’ stochastic. In this study, we estimate rye– ryegrass yield functions in which all parameters are random. The three functional forms considered are the linear response plateau, the quadratic, and the Spillman-Mitscherlich. Nonstochastic yield models are rejected in favor of stochastic parameter models. Quadratic functional forms fit the data poorly. Optimal nitrogen application recommendations are calculated for the linear response plateau and Spillman-Mitscherlich. The stochastic models lead to smaller recommended levels of nitrogen, but the economic benefits of using fully stochastic crop yield functions are small because expected profit functions are relatively flat for the stochastic yield functions. Stochastic crop yield functions provide a way of incorporating production, uncertainty into input decisions.cereal rye–ryegrass, Monte Carlo, nitrogen, random parameters, stochastic plateau, Production Economics, Q10, C12, D24,

Evaluation of an application for dynamic feeding of dairy cows

Dynamic feeding is an innovative application for concentrate feeding of dairy cows. Daily individual settings are derived from the actual individual milk yield response to concentrate intake. This response is estimated using an adaptive dynamic linear model. Optimal daily individual settings for concentrate supply are directed to achieve the maximum gross margin milk returns minus concentrate costs. This response curve plays a key role in the application. The response curve is derived from a mechanistic model for milk production and can also be established empirically from daily milk yield development during early lactation when concentrate supply increase is linear. A test application for dynamic feeding ran for several months in 2008 and results from 145 cows at one farm on 17 December 2008 have been used to demonstrate the variation in individual response. The gross margin, milk returns minus concentrate costs, varied from 2.52 to 26.32 €/day. The estimated response parameters provide insight in variation between individuals concerning the effects of concentrate and base ration intake on daily milk yield. Economical and nutritional aspects can be evaluated for each individual. Individual dynamic feeding towards an economic optimum indicates that excessive changes in individual bodyweighti can be prevented

YIELD RESPONSE IN PAKISTAN AGRICULTURE: A COINTEGRATION APPROACH

We seek to quantify and evaluate the supply (yield) response of wheat and cotton in Pakistan using cointegration analysis and annual data for 1960-96. The results reveal that wheat supply is significantly influenced by the prices of wheat, cotton, and fertilizer, the percentage area under high yielding wheat varieties, and the rabi season (winter) water availability. The cotton supply is found to be significantly influenced by the real cotton price, the real fertilizer price, and in the irrigated area. The wheat supply was found to be inelastic both in the short- and long-run. However, cotton supply was elastic in the long-run.Pakistan, supply response, wheat, cotton, cointegration., Crop Production/Industries,

An Innovative Approach for Modeling Crop Yield Response to Fertilizer Nutrients

Fertilizer recommendations seldom account for agro-climatic conditions, which are important factors that determine the response to fertilizer and the optimal rate of fertilizer. The nitrogen fertilizer response to open pollinated and hybrid canola types will also impact optimal nitrogen rates. This study used quantile regression to model canola yield response to nitrogen fertilizer. Quantile regression can apply different weights to the residuals, facilitating a response estimation where the agro-climatic conditions are not limiting and the yield response is due to the variable of interest. The economically optimal levels of fertilizers were calculated using the proposed and the conventional least squares procedures of the two canola types in western Canada. Results showed that the effects of nitrogen fertilizer on yield depended on the canola type and on the estimation procedure. Optimal levels of nitrogen for open-pollinated canola were estimated as 91, 115, and 134 kg ha-1 for severe, moderate and low levels of agro-climatic constraints. Hybrid had a higher yield potential, and also required more nitrogen fertilizer (137, 142, and 158 kg ha-1). Unlike conventional approach, proposed approach could benefit producer by recommending less (more) fertilizer when the crop response to fertilizer is expected to be low (high) due to agro-climatic conditions.Crop Production/Industries,