Armen Kemanian

Using a case study of the Yakima River Valley in Washington State, we show that relatively simple tools originally developed to forecast the impact of the El Nino phenomenon on water supplies to irrigated agriculture also can be used to estimate the significantly shifted probability distribution of water shortages in irrigated agriculture during climate change, and that these shifted probabilities can be used to estimate the impact on agriculture in a region. The more permanent nature of changes in the temperature and precipitation regime associated with climate change means that risk management options also take a more permanent form (such as changes in crops and cultivars, and adding storage). A number of storage options have been proposed to deal with El Nino-associated drought, and would be more valuable under climate change. The most ambitious of the proposed storage projects is Black Rock, which would add about 500,00 acre-feet of water to supplement the Yakima's current 1...

Transpiration-use efficiency, the ratio of biomass (Y) produced per unit of water transpired (T) by a crop, depends on crop characteristics and on the environment in which crops develop. Transpiration-use efficiency has been described as Y/T=kc/Da, where kc is a crop dependent constant and Da is the daytime air vapor pressure deficit. Our objectives were to determine Y/T and kc

Soil carbon cycling is an essential component of agroecosystems models. Simulating soil carbon (Cs) cycling has become an issue of societal importance for Cs storage can play a role reducing the rate of increase of atmospheric CO2 concentration. To participate in carbon trading markets, growers have to evaluate their local, site-specific options to increase Cs or reduce Cs losses. This

ABSTRACT Many simulation studies have been carried out to predict the effect of climate change on crop yield. Typically, in such study, one or several crop models are used to simulate series of crop yield values for different climate scenarios corresponding to different hypotheses of temperature, CO2 concentration, and rainfall changes. These studies usually generate large datasets including thousands of simulated yield data. The structure of these datasets is complex because they include series of yield values obtained with different mechanistic crop models for different climate scenarios defined from several climatic variables (temperature, CO2 etc.). Statistical methods can play a big part for analyzing large simulated crop yield datasets, especially when yields are simulated using an ensemble of crop models. A formal statistical analysis is then needed in order to estimate the effects of different climatic variables on yield, and to describe the variability of these effects across crop models. Statistical methods are also useful to develop meta-models i.e., statistical models summarizing complex mechanistic models. The objective of this paper is to present a random-coefficient statistical model (mixed-effects model) for analyzing large simulated crop yield datasets produced by the international project AgMip for several major crops. The proposed statistical model shows several interesting features; i) it can be used to estimate the effects of several climate variables on yield using crop model simulations, ii) it quantities the variability of the estimated climate change effects across crop models, ii) it quantifies the between-year yield variability, iv) it can be used as a meta-model in order to estimate effects of new climate change scenarios without running again the mechanistic crop models. The statistical model is first presented in details, and its value is then illustrated in a case study where the effects of climate change scenarios on different crops are compared.