Effects of heat and drought stress on cereal crops across spatial scales

The production of cereal crops is increasingly influenced by heat and drought stress. Despite the typical small-scale sub-regional variability of these stresses, impacts on yields are also of concern at larger regional to global scales. Crop growth models are the most widely used tools for simulating the effects of heat and drought stress on crop yield. However, the development and application of crop models to simulate heat and drought is still a challenging issue, particularly their application at larger spatial scales. Previous research showed that there is a lack of information regarding the: 1. Response of cereal crops to heat stress, 2. Interactions between phenology and heat stress under climate change, 3. Improvement of crop models for reproducing heat stress effects on crop yield, 4. Upscaling of heat and drought stress effects with crop models, 5. Effects of climate and management interactions on crop yield in semi-arid environments. Five detailed studies were arranged to improve the understanding on the aforementioned gaps of knowledge: 1. A review study was set up to understand how crop growth processes responded to short episodes of high temperature. In addition, the possible ways for improvement of the heat stress simulation algorithms in crop models were investigated at a field scale. The reproductive phase of development in cereals was found to be the most sensitive phase to heat stress. Crop models aiming to model heat stress effects on crops under field conditions should consider the modelling of canopy temperature. This may also provide a mechanistic basis to link heat and drought stress in crop models. Generally, these two stresses occur simultaneously. 2. In a nationwide study, the interactions between the advancements of phenology and heat stress on winter wheat (Triticum aestivum L.) due to global warming, were evaluated between1951-2009 across Germany. The increase in temperature (~1.8°C) shifted crop phenology to cooler parts of the growing season (~14 days) and compensated for the effect of global warming on heat stress intensity in the period 1976-2009. The intensity of heat stress on winter wheat could have increased by up to 59% without any advancement in phenology. 3. A large-scale simulation study was conducted to investigate the effects of input (climate and soil) and output data aggregation on simulated heat and drought stress for winter wheat over the period of 1980-2011 across Germany. Aggregation levels were compared in several steps from 1 km × 1 km to 100 km × 100 km. Simulations were performed with SIMPLACE. Aggregation of weather and soil data showed a slight impact on the mean and median of simulated heat and drought stress at the national scale. No remarkable differences in simulated mean yields of winter wheat were evident for the different resolutions ranging from 1 km × 1 km to 100 km × 100 km across Germany. However, high resolution input data was essential to reproduce spatial variability of heat and drought stress for the more heterogeneous regions. 4. Two regional studies were arranged to evaluate the interactions between management and climate on crop production under climate change conditions. A crop model (DSSAT v4.5) was employed to assess the interactions between fertilization management of pearl millet (Pennisetum americanum L.), crop substitution [pearl millet instead of maize (Zea mays L)], and climate in semi-arid environments of Iran and the Republic of Niger, respectively. The pearl millet biomass production showed a strong response to different fertilization management in Niger. The highest dry matter production of pearl millet was obtained in combination with crop residues and mineral fertilizer treatment. The dry matter production of pearl millet was reduced by 11% to 62% under different climate change scenarios and future time periods (2011-2030 and 2080-2099). Results of this study showed that higher soil fertility could compensate for the negative effects of high temperature on biomass production. This was a result of the strong positive relationship between biomass production and the sum of precipitation under high soil fertility. Crop substitution as an adaptation strategy (new hybrids of pearl millet instead of maize) enhanced fodder production and water use efficiency in present and potential future climatic conditions in northeast Iran. However, the fodder production of both crops was reduced due to shortening of the period from floral initiation to the end of leaf growth under various climate change conditions. Benefits of crop substitution may decline under climate change resulting in higher temperature sensitivity of the new hybrids of pearl millet. Several conclusions were drawn from this study: It is necessary to consider canopy temperature instead of air temperature in crop models and use data from experiments under field conditions to improve and properly calibrate crop models for heat and drought stress responses. Crop models must also consider that effects of heat and drought stress on crops differ with phenological phases and can be compensated for by responses of other processes. An increase in the intensity of heat stress around anthesis can, for instance, be fully compensated for by the advancement in phenology in winter cereals under climate change. It is not necessary to use high resolution weather and soil input data for simulating the effects of heat and drought stress on crop yield at a national scale; but, high resolution input data are necessary to reproduce spatial patterns of heat and drought. Finally, implementation of management practices in cropping systems may change the response of crops to climate change. For this reason, management practices should be considered as an adaptation strategy

Impacts of climate change on rice production in Africa and causes of simulated yield changes

This study is the first of its kind to quantify possible effects of climate change on rice production in Africa. We simulated impacts on rice in irrigated systems (dry season and wet season) and rainfed systems (upland and lowland). We simulated the use of rice varieties with a higher temperature sum as adaptation option. We simulated rice yields for 4 RCP climate change scenarios and identified causes of yield declines. Without adaptation, shortening of the growing period due to higher temperatures had a negative impact on yields (−24% in RCP 8.5 in 2070 compared with the baseline year 2000). With varieties that have a high temperature sum, the length of the growing period would remain the same as under the baseline conditions. With this adaptation option rainfed rice yields would increase slightly (+8%) but they remain subject to water availability constraints. Irrigated rice yields in East Africa would increase (+25%) due to more favourable temperatures and due to CO2 fertilization. Wet season irrigated rice yields in West Africa were projected to change by −21% or +7% (without/with adaptation). Without adaptation irrigated rice yields in West Africa in the dry season would decrease by −45% with adaptation they would decrease significantly less (−15%). The main cause of this decline was reduced photosynthesis at extremely high temperatures. Simulated heat sterility hardly increased and was not found a major cause for yield decline. The implications for these findings are as follows. For East Africa to benefit from climate change, improved water and nutrient management will be needed to benefit fully from the more favourable temperatures and increased CO2 concentrations. For West Africa, more research is needed on photosynthesis processes at extreme temperatures and on adaptation options such as shifting sowing dates

A process-based crop growth model for assessing Global Change effects on biomass production and water demand - A component of the integrative Global Change decision support system DANUBIA -

Spatial and temporal changes in crop water demand are of fundamental significance when examining potential impacts of Global Change on water resources on the regional scale. Carried out within the project GLOWA-Danube, this study investigates the response of crops to changing environmental conditions as well as to agricultural management. As a component of the integrative Global Change decision support system DANUBIA, a process-based crop growth model was developed by combining the models GECROS and CERES. The object-oriented, generic model comprises sugar beet, spring barley, maize, winter wheat and potato. The modelled processes are valid for all crops and mainly comprise phenological development, photosynthesis, transpiration, respiration, nitrogen demand, root growth, soil layer-specific water and nitrogen uptake, allocation of carbon and nitrogen as well as leaf area development and senescence. Attention is given to crop-specific differences through assignment to crop categories (e.g. C4 photosynthesis type) and a set of crop-specific parameters. The model was validated by comparing simulated data with several sets of field measurements, covering a wide range of meteorological and pedological conditions in Germany. Furthermore, the responsiveness of the model to Global Change effects was examined in terms of increased air temperatures and atmospheric carbon dioxide concentrations. The results show that the model efficiently simulates crop development and growth and adequately responds to Global Change effects. The crop growth model is therefore a suitable tool for numerically assessing the consequences of Global Change on biomass production and water demand, taking into account the complex interplay of water, carbon and nitrogen fluxes in agro-ecosystems. Within DANUBIA, the model will contribute to the development of effective strategies for a sustainable management of water resources in the Upper Danube Basin

An evaluation of four crop : weed competition models using a common data set

To date, several crop : weed competition models have been developed. Developers of the various models were invited to compare model performance using a common data set. The data set consisted of wheat and Lolium rigidum grown in monoculture and mixtures under dryland and irrigated conditions. Results from four crop : weed competition models are presented: ALMANAC, APSIM, CROPSIM and INTERCOM. For all models, deviations between observed and predicted values for monoculture wheat were only slightly lower than for wheat grown in competition with L. rigidum, even though the workshop participants had access to monoculture data while parameterizing models. Much of the error in simulating competition outcome was associated with difficulties in accurately simulating growth of individual species. Relatively simple competition algorithms were capable of accounting for the majority of the competition response. Increasing model complexity did not appear to dramatically improve model accuracy. Comparison of specific competition processes, such as radiation interception, was very difficult since the effects of these processes within each model could not be isolated. Algorithms for competition processes need to be modularized in such a way that exchange, evaluation and comparison across models is facilitated

Crop modeling for assessing and mitigating the impacts of extreme climatic events on the US agriculture system

The US agriculture system is the world’s largest producer of maize and soybean, and typically supplies more than one-third of their global trading. Nearly 90% of the US maize and soybean production is rainfed, thus is susceptible to climate change stressors such as heat waves and droughts. Process-based crop and cropping system models are important tools for climate change impact assessments and risk management. As data- science is becoming a new frontier for agriculture growth, the incoming decade calls for operational platforms that use hyper-local growth monitoring, high-resolution real-time weather and satellite data assimilation and cropping system modeling to help stakeholders predict crop yields and make decisions at various spatial scales. The fundamental question addressed by this dissertation is: How crop and cropping system models can be “useful” to the agriculture production, given the recent advent of cloud computing and earth observatory power? This dissertation consists of four main chapters. It starts with a study that reviews the algorithms of simulating heat and drought stress on maize in 16 major crop models, and evaluates algorithm performances by incorporating these algorithms into the Agricultural Production Systems sIMulator (APSIM) and running an ensemble of simulations at typical farms from the US Midwest. Results show that current parameterizations in most models favor the use of daylight temperature even though the algorithm was designed for using daily mean temperature. Different drought algorithms considerably differed in their patterns of water shortage over the growing season, but nonetheless predicted similar decreases in annual yield. In the next chapter of climate change assessment study, I quantify the current and future yield responses of US rainfed maize and soybean to climate extremes with and without considering the effect of elevated atmospheric CO2concentrations, and for the first time characterizes spatial shifts in the relative importance of temperature, heat and drought stresses. Model simulations demonstrate that drought will continue to be the largest threat to rainfed maize and soybean production, yet shifts in the spatial pattern of dominant stressors are characterized by increases in the concurrent stress, indicating future adaptation strategies will have trade-offs between multiple objectives. Following this chapter, I presented a chapter that uses billion-scale simulations to identify the optimal combination of Genotype × Environment × Management for the purpose of minimizing the negative impact of climate extremes on the rainfed maize yield. Finally, I present a prototype of crop model and satellite imagery based within-field scale N sidedress prescription tool for the US rainfed maize system. As an early attempt to integrate advances in multiple areas for precision agriculture, this tool successfully captures the subfield variability of N dynamics and gives reasonable spatially explicit sidedress N recommendations. The prescription enhances zones with high yield potentials, while prevents over-fertilization at zones with low yield potentials

Modelling crop yield in a wheat-soybean relay intercropping system: A simple routine in capturing competition for light

Moving from sole cropping to intercropping is a transformative change in agriculture, contributing to several ecosystem services. However, modelling intercropping is challenging due to intensive parameterisation, complex calibration, and experiment scarcity. To facilitate future understanding, design and adaptation of intercropping, it is therefore necessary to develop simple modelling routines capable of simulating essential features. In this paper, we integrated a light competition module requiring four parameters into MONICA, a generic agroecosystem model, with the goal of simulating a wheat-soybean relay-row intercropping system. We tested three calibration approaches using data from two years of field experiments located in Muncheberg, Germany: sole cropping-based calibration, intercropping-based calibration and a default calibration method that incorporates both systems. Under both irrigated and rainfed conditions, MONICA successfully reproduced the aboveground biomass and yield of sole crops from field experiments, with RMSEA ranging from 0.64 t ha-1 to 2.74 t ha-1 and RMSEY ranging from 0.003 t ha-1 to 0.47 t ha-1. By taking light competition into account, the modified MONICA was able to simulate interactive performance in relay-row intercropping. Generally, MONICA overestimated the aboveground biomass and yield across the three calibration strategies, and simulations for wheat were more accurate than those for soybean. However, a comparison among the calibration strategies revealed that the intercropping-based strategy outperformed the others. It significantly improved the model efficiency for soybean yield in intercropping, increasing the Index of Agreement from 0.27 to 0.73, and it decreased the Mean Bias Error for yield by up to 76%. Our results demonstrate the feasibility of using a model that is simple in both calibration and inputs, yet detailed enough to simulate the complex aboveground light competition of intercropping. Additionally, they underscore the significance of cropping system specific calibration, highlighting the importance of calibrating crop performance specifically for intercropping in order to capture genotype-by-environment interactions