Raymond Jongschaap

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.

High level of molecular and phenotypic biodiversity in Jatropha curcas from Central America compared to Africa, Asia and South America

BackgroundThe main bottleneck to elevate jatropha (Jatropha curcas L.) from a wild species to a profitable biodiesel crop is the low genetic and phenotypic variation found in different regions of the world, hampering efficient plant breeding for productivity traits. In this study, 182 accessions from Asia (91), Africa (35), South America (9) and Central America (47) were evaluated at genetic and phenotypic level to find genetic variation and important traits for oilseed production.ResultsGenetic variation was assessed with SSR (Simple Sequence Repeat), TRAP (Target Region Amplification Polymorphism) and AFLP (Amplified fragment length polymorphism) techniques. Phenotypic variation included seed morphological characteristics, seed oil content and fatty acid composition and early growth traits. Jaccard’s similarity and cluster analysis by UPGM (Unweighted Paired Group Method) with arithmetic mean and PCA (Principle Component Analysis) indicated higher variability in Central American accessions compared to Asian, African and South American accessions. Polymorphism Information Content (PIC) values ranged from 0 to 0.65. In the set of Central American accessions. PIC values were higher than in other regions. Accessions from the Central American population contain alleles that were not found in the accessions from other populations. Analysis of Molecular Variance (AMOVA; P < 0.0001) indicated high genetic variation within regions (81.7%) and low variation across regions (18.3%). A high level of genetic variation was found on early growth traits and on components of the relative growth rate (specific leaf area, leaf weight, leaf weight ratio and net assimilation rate) as indicated by significant differences between accessions and by the high heritability values (50–88%). The fatty acid composition of jatropha oil significantly differed (P < 0.05) between regions.ConclusionsThe pool of Central American accessions showed very large genetic variation as assessed by DNA-marker variation compared to accessions from other regions. Central American accessions also showed the highest phenotypic variation and should be considered as the most important source for plant breeding. Some variation in early growth traits was found within a group of accessions from Asia and Africa, while these accessions did not differ in a single DNA-marker, possibly indicating epigenetic variation.

Identification of QTL markers contributing to plant growth, oil yield and fatty acid composition in the oilseed crop Jatropha curcas L.

AbstractBackgroundEconomical cultivation of the oilseed crop Jatropha curcas is currently hampered in part due to the non-availability of purpose-bred cultivars. Although genetic maps and genome sequence data exist for this crop, marker-assisted breeding has not yet been implemented due to a lack of available marker–trait association studies. To identify the location of beneficial alleles for use in plant breeding, we performed quantitative trait loci (QTL) analysis for a number of agronomic traits in two biparental mapping populations.ResultsThe mapping populations segregated for a range of traits contributing to oil yield, including plant height, stem diameter, number of branches, total seeds per plant, 100-seed weight, seed oil content and fatty acid composition. QTL were detected for each of these traits and often over multiple years, with some variation in the phenotypic variance explained between different years. In one of the mapping populations where we recorded vegetative traits, we also observed co-localization of QTL for stem diameter and plant height, which were both overdominant, suggesting a possible locus conferring a pleotropic heterosis effect. By using a candidate gene approach and integrating physical mapping data from a recent high-quality release of the Jatropha genome, we were also able to position a large number of genes involved in the biosynthesis of storage lipids onto the genetic map. By comparing the position of these genes with QTL, we were able to detect a number of genes potentially underlying seed traits, including phosphatidate phosphatase genes.ConclusionsThe QTL we have identified will serve as a useful starting point in the creation of new varieties of J. curcas with improved agronomic performance for seed and oil productivity. Our ability to physically map a significant proportion of the Jatropha genome sequence onto our genetic map could also prove useful in identifying the genes underlying particular traits, allowing more controlled and precise introgression of desirable alleles and permitting the pyramiding or stacking of multiple QTL.

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