Hendrik Boogaard

How do various maize crop models vary in their responses to climate change factors

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

Assessing relative soil moisture with remote sensing data : theory, experimental validation, and applicatIon to drought monitoring over the north China plain

Based on physical consideration of land surface energy balance, a theory is proposed for assessing relative soil moisture in the rooting depth with remote sensing data. The basis of the theory is the derivation of relative soil moisture in the rooting zone which is theoretically shown to be derivable from relative evaporation. The relationship derived between the relative soil moisture and relative evaporation is confirmed with experimental data collected with lysimeter measurements and in intensive field campaigns. Further it is shown that the proposed theory can be used to define a drought severity index (DSI) for drought monitoring, when the relative evaporation can be determined with remote sensing data. For this purpose, a demonstration in North China is performed. The used remote sensing data are NOAA/AVHRR which is available on a daily basis. the required meteorological data (wind speed. air temperature. humidity and pressure) are obtained from the operational measurement network of the National Meteorological Center of China. Comparisons between the estimated DSI and the actual measurements of soil moisture confirm the validity and robustness of the proposed theory

Kenya public weather processed by the Global Yield Gap Atlas project

The Global Yield Gap Atlas project (GYGA - http://yieldgap.org ) has undertaken a yield gap assessment following the protocol recommended by van Ittersum et. al. (van Ittersum et. al., 2013). One part of the activities consists of collecting and processing weather data as an input for crop simulation models in sub-Saharan African countries including Kenya. This publication covers weather data for 10 locations in Kenya. The project looked for good quality weather data in areas where crops are pre-dominantly grown. As locations with good public weather data are sparse in Africa, the project developed a method to generate weather data from a combination of observed and other external weather data. One locations holds actually measured weather data, the other 9 locations show propagated weather data. The propagated weather data consist on TRMM rain data (or NASA POWER if TRMM is not available) and NASA POWER Tmax, Tmin, and Tdew data corrected based on calibrations with short-term (<10 years) observed weather data. sources (Van Wart et.al. 2015).

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

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. See more from this Division: Special Sessions See more from this Session: Symposium--Perspectives on Climate Effects on Agriculture: The International Efforts of AgMIP