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.

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Crop growth models dynamically simulate processes of C, N and water balance on daily or hourly time-steps to predict crop growth and development and at season-end, final yield. Their ability to integrate effects of genetics, environment and crop management have led to applications ranging from understanding gene function to predicting potential impacts of climate change. The history of crop models is reviewed briefly, and their level of mechanistic detail for assimilation and respiration, ranging from hourly leaf-to-canopy assimilation to daily radiation-use efficiency is discussed. Crop models have improved steadily over the past 30-40 years, but much work remains. Improvements are needed for the prediction of transpiration response to elevated CO2 and high temperature effects on phenology and reproductive fertility, and simulation of root growth and nutrient uptake under stressful edaphic conditions. Mechanistic improvements are needed to better connect crop growth to genetics and to soil fertility, soil waterlogging and pest damage. Because crop models integrate multiple processes and consider impacts of environment and management, they have excellent potential for linking research from genomics and allied disciplines to crop responses at the field scale, thus providing a valuable tool for deciphering genotype by environment by management effects.

Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984–2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO2 offering no yield benefit in these years.

Heat stress is a main threat to current and future global maize production. Adaptation of maize to future warmer conditions requires improving our understanding of crop responses to elevated temperatures. For this purpose, the same short-season (FAO 300) maize hybrid PR37N01 was grown over three years of field experiments on three contrasting Spanish locations in terms of temperature regime. The information complemented three years of greenhouse experiments with the same hybrid, applying heat treatments at various critical moments of the crop cycle. Crop phenology, growth, grain yield, and yield components were monitored. An optimized beta function improved the calculation of thermal time compared to the linear-cutoff estimator with base and optimum temperatures of 8 and 34 °C, respectively. Our results showed that warmer temperatures accelerate development rate resulting in shorter vegetative and reproductive phases (ca. 30 days for the whole cycle). Heat stress did not cause silking delay in relation to anthesis (extended anthesis-silking interval), at least in the range of temperatures (maximum temperature up to 42.9 °C in the field and up to 52.5 °C in the greenhouse) considered in this study. Our results indicated that maize grain yield is reduced under heat stress mainly via pollen viability that in turn determines kernel 2 number, although a smaller but significant effect of the female component has been also detected.

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Accurate prediction of phenological development in maize (Zea mays L.) is fundamental to determining crop adaptation and yield potential. A number of thermal functions are used in crop models, but their relative precision in predicting maize development has not been quantified. The objectives of this study were (i) to evaluate the precision of eight thermal functions, (ii) to assess the effects of source data on the ability to differentiate among thermal functions, and (iii) to attribute the precision of thermal functions to their response across various temperature ranges. Data sets used in this study represent >1000 distinct maize hybrids, >50 geographic locations, and multiple planting dates and years. Thermal functions and calendar days were evaluated and grouped based on their temperature response and derivation as empirical linear, empirical nonlinear, and process-based functions. Precision in predicting phase durations from planting to anthesis or silking and from silking to physiological maturity was evaluated. Large data sets enabled increased differentiation of thermal functions, even when smaller data sets contained orthogonal, multi-location and -year data. At the highest level of differentiation, precision of thermal functions was in the order calendar days < empirical linear < process based < empirical nonlinear. Precision was associated with relatively low temperature sensitivity across the 10 to 26°C range. In contrast to other thermal functions, process-based functions were derived using supra-optimal temperatures, and consequently, they may better represent the developmental response of maize to supra-optimal temperatures. Supra-optimal temperatures could be more prevalent under future climate-change scenarios, but data sets in this study contained few data in that range.

fertile (MF)], and top crosses (approximately 10% MF), coupled with environmental effects on pollen produc-Pollen production generally is not considered a limiting factor in tion and viability (Westgate and Bassetti, 1991) provide modern maize (Zea mays L.) production. In some modern hybrids, however, smaller tassel size, use of male sterile blends, and top cross numerous opportunities for pollen production to limit production may limit pollen availability. This study was conducted to kernel set under field conditions. establish the lower limits for pollen production needed to ensure Published estimates of maize pollen production on a maximum kernel set, and to devise a method for predicting pollen field scale are very limited, particularly in relation to production from simple measures of tassel development. Isolated kernel formation. The lack of quantitative information blocks of male fertile plants were established in a field of male sterile on pollen production reflects the laborious nature of plants. The level of male fertility was varied from 0 to 100% by the techniques used to quantify pollen shed, such as detasselling (Pioneer 3978) or by using mixtures of male fertile and spinning rods (Flottum et al., 1984), tassel bags (Sadras male sterile isolines (Pioneer 3925 and 3925S). Pollen shed was meaet al., 1985a), and passive pollen traps (Bassetti and sured daily with passive pollen traps. A Population Index, derived Westgate, 1994; Uribelarrea et al., 2002), and the need from the percentage of plants shedding pollen and the average pollen production per plant, accurately predicted the seasonal pattern and to collect samples daily to generate a seasonal integral total pollen shed for each male fertility treatment. Hybrids used in of pollen production. These approaches for quantifying this study shed pollen for 10 to 12 d, with a peak intensity 2 to pollen production, however, have not taken full advan-3 d after anthesis (50% plants shedding pollen). Individual tassels tage of the predictable nature of tassel development and produced 4.5 ϫ 10 6 pollen grains on average, and shed pollen for 5 the process of pollen shed from the staminate flowers or 6 d. Variation in grain yield across male fertility treatments was (Kiesselbach, 1999). closely correlated to kernels per plant (r 2 ϭ 0.998). Seasonal pollen Hybrid seed production requires close synchrony beproduction limited kernels per plant at pollen densities less than 3000 tween receptive silks on the female parent and pollen pollen grains silk Ϫ1 . This lower limit for effective kernel set occurred shed by the male parent. Abundant pollen is critical for at male fertility levels less than 50% for both hybrids. Grain yield, high seed set and genetic purity (Wych, 1988). Establish-Pollen shed density treatments were established within a 40-by 60-m field block at Morris, MN, on a Sioux sandy loam