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.

No abstract

A first‐generation energy sorghum hybrid with enhanced photoperiod sensitivity and long growth duration accumulated more than twice as much biomass as grain sorghum. The energy sorghum produced more leaves (~45 vs 17–20), longer stems (~4 vs 1.5 meters) and had a higher stem‐to‐leaf biomass ratio than grain sorghum. At the end of the season, energy sorghum stems represented 83% of the plant's shoot biomass. The greater biomass accumulation was due to longer growth duration, a higher leaf area index, greater radiation interception, and higher radiation use efficiency. When grown under dryland or limited irrigation conditions, the rate of biomass accumulation by the energy sorghum hybrid was reduced in mid‐season. This decrease was most apparent in August due to summer water deficit; however plants recovered when rain occurred in September. Crop growth modeling and biomass accumulation rates measured under optimal field conditions show that energy sorghum, like other C4 energy grasses, has excellent biomass yield potential. The greenhouse gas offset values of energy sorghum hybrids, grown in large and small field plots, and under fully irrigated and dryland conditions, ranged from 63–78% for cellulosic ethanol production and 88–95% for power generation. This study shows that drought‐tolerant, annual energy sorghum hybrids have the genetic yield potential to contribute significantly to bioenergy production. Sorghum is a genetically tractable, diverse species with a good genomics platform, making energy sorghum a promising genetic model for the design of C4 grass energy crops. © 2012 Society of Chemical Industry and John Wiley & Sons, Ltd

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.

Climate change may both exacerbate the vulnerabilities and open up new opportunities for farming in the Northeastern USA. Among the opportunities are double-cropping and new crop options that may come with warmer temperatures and a longer frost-free period. However, prolonged periods of spring rains in recent years have delayed planting and offset the potentially beneficial longer frost-free period. Water management will be a serious challenge for Climatic Change (2018) Northeast farmers in the future, with projections for increased frequency of heavy rainfall events, as well as projections for more frequent summer water deficits than this historically humid region has experienced in the past. Adaptations to increase resilience to such changes include expanded irrigation capacity, modernized water monitoring and irrigation scheduling, farm drainage systems that collect excess rain into ponds for use as a water source during dry periods, and improved soil water holding capacity and drainage. Among the greatest vulnerabilities over the next several decades for the economically important perennial fruit crop industry of the region is an extended period of spring frost risk associated with warmer winter and early spring temperatures. Improved real-time frost warning systems, careful site selection for new plantings, and use of misting, wind machine, or other frost protection measures will be important adaptation strategies. Increased weed and pest pressure associated with longer growing seasons and warmer winters is another increasingly important challenge. Pro-active development of non-chemical control strategies, improved regional monitoring, and rapidresponse plans for targeted control of invasive weeds and pests will be necessary.

No abstract

Food security and agriculture productivity assessments in sub-Saharan Africa (SSA) require a better understanding of how climate and other drivers influence regional crop yields. In this paper, our objective was to identify the climate signal in the realized yields of maize, sorghum, and groundnut in SSA. We explored the relation between crop yields and scale-compatible climate data for the 1962-2014 period using Random Forest, a diagnostic machine learning technique. We found that improved agricultural technology and country fixed effects are three times more important than climate variables for explaining changes in crop yields in SSA. We also found that increasing temperatures reduced yields for all three crops in the temperature range observed in SSA, while precipitation increased yields up to a level roughly matching crop evapotranspiration. Crop yields exhibited both linear and nonlinear responses to temperature and precipitation, respectively. For maize, technology steadily increased yields by about 1% (13 kg/ha) per year while increasing temperatures decreased yields by 0.8% (10 kg/ha) per °C. This study demonstrates that although we should expect increases in future crop yields due to improving technology, the potential yields could be progressively reduced due to warmer and drier climates.

No abstract