Ehsan Eyshi Rezaei

Impact of heat stress on crop yield—on the importance of considering canopy temperature

Increasing crop productivity while simultaneously reducing the environmental footprint of crop production is considered a major challenge for the coming decades. Even short episodes of heat stress can reduce crop yield considerably causing low resource use efficiency. Studies on the impact of heat stress on crop yields over larger regions generally rely on temperatures measured by standard weather stations at 2 m height. Canopy temperatures measured in this study in field plots of rye were up to 7 °C higher than air temperature measured at typical weather station height with the differences in temperatures controlled by soil moisture contents. Relationships between heat stress and grain number derived from controlled environment studies were only confirmed under field conditions when canopy temperature was used to calculate stress thermal time. By using hourly mean temperatures measured by 78 weather stations located across Germany for the period 1994–2009 it is estimated, that mean yield declines in wheat due to heat stress during flowering were 0.7% when temperatures are measured at 2 m height, but yield declines increase to 22% for temperatures measured at the ground. These results suggest that canopy temperature should be simulated or estimated to reduce uncertainty in assessing heat stress impacts on crop yield.

Adaptation of crop production to climate change by crop substitution

Research on the impact of climate change on agricultural production has mainly focused on the effect of climate and its variability on individual crops, while the potential for adapting to climate change through crop substitution has received less attention. This is surprising because the proportions of individual crops in the total crop area have changed considerably over periods of time much shorter than those typically investigated in climate change studies. The flexibility of farmers to adapt to changing socioeconomic and environmental conditions by changing crop type may therefore also represent an alternative option to adapt to climate change. The objective of this case study was to investigate the potential of crop substitution as an adaptation strategy to climate change. We compared biomass yield and water use efficiency (WUE) of maize (Zea mays L) and pearl millet (Pennisetum americanum L.) grown in the semi-arid northeast of Iran for fodder production under present and potential future climatic conditions. Climate change projections for the baseline period 1970–2005 and two future time periods (2011–2030 and 2080–2099) from two emission scenarios (A2 and B1) and four general circulation models were downscaled to daily time steps using the Long Ashton Research Station-Weather Generator (LARS-WG5). Above-ground biomass was simulated for seven research sites with the Decision Support System for Agrotechnology Transfer (DSSAT 4.5) model which was calibrated and tested with independent experimental data from different field experiments in the region. The analysis of observations across all study locations showed an inverse relationship between temperature and biomass yield for both pearl millet and maize. Biomass yield was most sensitive to the duration of the phenological phase from floral initiation to end of leaf growth. For this phase we also found the highest negative correlation between mean temperature and biomass yield, which was more pronounced for pearl millet than for maize. This relationship was well reproduced by the crop model, justifying its use for the assessment. Due to the higher sensitivity of pearl millet to temperature increase, simulations suggest that the maximum benefit of crop substitution for biomass yield and WUE is to be gained for present-day conditions and would decline under future warming. The simulated increase in biomass yield due to substitution of maize by pearl millet was nevertheless larger than the yield decrease from potential climate change. Therefore, substituting maize by pearl millet should be considered as a measure for increasing fodder production in the investigated region. Differences in yields of crops that may substitute for each other because of similar use have been shown for other regions under current and potential future climatic conditions as well, so that we suggest that our findings are of general importance for climate change research. More research is required to quantify the effects for other crop combinations, regions, and interactions with other adaptation measures.

Evaluation of Ceres-Rice, Aquacrop and Oryza2000 Models in Simulation of Rice Yield Response to Different Irrigation and Nitrogen Management Strategies

This study evaluated CERES-Rice, AquaCrop, and ORYZA2000 models performance in simulation of biological and grain yield of rice in response to different irrigation intervals and nitrogen levels. These models were calibrated and validated by using three years (2005 to 2007) field experiments. Three levels of irrigation interval included pond treatment, five days interval, and eight days interval, and consisted of four levels of nitrogen. The study results showed that there were significant differences among study crop models in simulation of grain and biological yield in response to different irrigation intervals. As results showed, study models performed more accurate in estimation of rice yield under irrigation intervals than nitrogen levels. All models illustrated high performance in estimation of rice yield under different irrigation intervals. CERES-Rice and AquaCrop models showed highest accuracy in simulation of grain and biological yield of rice under different levels of nitrogen, respectively. In addition, CERES-Rice model indicated highest performance in simulation of grain yield (rRMSE = 16). However, AquaCrop model estimated biological yield more accurate compared to other models (rRMSE = 15). ORYZA2000 showed less accurate in simulating grain (rRMSE = 23) and biological (rRMSE = 21) yield of rice in comparison with other models.

Climate change effect on wheat phenology depends on cultivar change

Changing crop phenology is considered an important bio-indicator of climate change, with the recent warming trend causing an advancement in crop phenology. Little is known about the contributions of changes in sowing dates and cultivars to long-term trends in crop phenology, particularly for winter crops such as winter wheat. Here, we analyze a long-term (1952–2013) dataset of phenological observations across western Germany and observations from a two-year field experiment to directly compare the phenologies of winter wheat cultivars released between 1950 and 2006. We found a 14–18% decline in the temperature sum required from emergence to flowering for the modern cultivars of winter wheat compared with the cultivars grown in the 1950s and 1960s. The trends in the flowering day obtained from a phenology model parameterized with the field observations showed that changes in the mean temperature and cultivar properties contributed similarly to the trends in the flowering day, whereas the effects of changes in the sowing day were negligible. We conclude that the single-cultivar concept commonly used in climate change impact assessments results in an overestimation of winter wheat sensitivity to increasing temperature, which suggests that studies on climate change effects should consider changes in cultivars.

Multimodel ensembles improve predictions of crop–environment–management interactions

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.

Author Correction: The uncertainty of crop yield projections is reduced by improved temperature response functions

Nature Plants3, 17102 (2017); published online 17 July 2017; corrected online 27 September 2017.

Nitrogen and Cultivated Bulb Weight Effects on Radiation and Nitrogen-Use Efficiency, Carbon Partitioning and Production of Persian Shallot (Allium altissimum Regel.)

Persian shallot (Allium altissimum Regel.) was grown under fully irrigated conditions in a 2-year-field experiment (2010–2012) in the northeast of Iran to study and determine (i) radiation and nitrogen-use efficiency, (ii) growth analysis, (iii) carbon partitioning, and (iv) biomass production under different rates of nitrogen and cultivated bulb weights. The field experiment was performed as a randomized complete block design with a factorial arrangement of four nitrogen levels (control (100), 200, 250, and 300 kg ha−1) and two levels of cultivated bulb weight (10–20 and 20–30 g) with three replications in both years of the experiment. Our results showed that increasing the nitrogen rate and bulb weight significantly enhanced Persian shallot production. Radiation-use efficiency (1.06 to 1.27 g MJ−1), maximum crop growth rate (8.3 to 11.2 g m−2 d−1), and maximum leaf area index (1.3 to 2.6) showed a positive correlation with nitrogen rate and bulb weight. Nevertheless, nitrogen-use efficiency (0.87 to 2.38 g bulb per g nitrogen) indicated a negative relationship with applied nitrogen rate. Moreover, increasing the nitrogen application rate increased the carbon allocation to above-ground organs. On the other hand, nitrogen limited conditions increased the carbon allocation to underground organs and carbon remobilization from stem and leaves to bulbs during the late growth season. Increasing the nitrogen application rate and bulb weight may be appropriate practices for enhancing Persian shallot production; however, evaluation of the impact of nitrogen on the quality of bulbs needs to be investigated.