Climate Change Impact on Yield and Water Use of Rice-Wheat Rotation System in the Huang-Huai-Hai Plain, China

Global climate change has had a significant impact on crop production and agricultural water use. Investigating different future climate scenarios and their possible impacts on crop production and water consumption is critical for proposing effective responses to climate change. In this study, based on daily downscaled climate data from 22 Global Climate Models (GCMs) provided by Coupled Model Intercomparison Project Phase 6 (CMIP6), we applied the well-validated Agricultural Production Systems sIMulator (APSIM) to simulate crop phenology, yield, and water use of the rice-wheat rotation at four representative stations (including Hefei and Shouxian stations in Anhui province and Kunshan and Xuzhou stations in Jiangsu province) across the Huang-Huai-Hai Plain, China during the 2041-2070 period (2050s) under four Shared Socioeconomic Pathways (i.e., SSP126, SSP245, SSP370, and SSP585). The results showed a significant increase in annual mean temperature (Temp) and solar radiation (Rad), and annual total precipitation (Prec) at four investigated stations, except Rad under SSP370. Climate change mainly leads to a consistent advance in wheat phenology, but inconsistent trends in rice phenology across four stations. Moreover, the reproductive growth period (RGP) of wheat was prolonged while that of rice was shorted at three of four stations. Both rice and wheat yields were negatively correlated with Temp, but positively correlated with Rad, Prec, and CO₂ concentration ([CO2]). However, crop ET was positively correlated with Rad, but negatively correlated with [CO2], as elevated [CO2] decreased stomatal conductance. Moreover, the water use efficiency (WUE) of rice and wheat was negatively correlated with Temp, but positively correlated with [CO2]. Overall, our study indicated that the change in Temp, Rad, Prec, and [CO2] have different impacts on different crops and at different stations. Therefore, in the impact assessment for climate change, it is necessary to explore and analyze different crops in different regions. Additionally, our study helps to improve understanding of the impacts of climate change on crop production and water consumption and provides data support for the sustainable development of agriculture.

Quantifying and optimizing agroecosystem services in China's Taihu Lake Basin

This study examines rice-wheat agroecosystems in the Taihu Lake Basin: one of China's largest commercial grain-farming areas and a region that has faced severe deterioration in water quality. Spatiotemporal changes over the period 1986-2015 in four key ecosystem services (ESs) - grain yield, nitrogen loss, N₂O emission, and soil organic carbon (SOC) accumulation - were examined by applying the Agricultural Production Systems Simulator (APSIM) across the basin at county level. Two straw return modes (namely, full straw return versus no return) and three fertilizer-use reduction modes (-5%, -10%, and -20%) were set up to generate six combined scenarios, to propose pathways that reduce the variability of grain production and improve water quality by reducing loss of nitrogen (N loss) - in consideration of the Basin's vital role in agricultural production and the need to protect water quality. Results show that annual grain yield and net five-year difference in SOC accumulation exhibited an overall downward trend from 1986 to 2015, while N₂O emission and N loss increased. Two pairs of ESs showed desirable synergies (increasing grain yield and increasing SOC accumulation; decreasing N₂O emission and decreasing N loss), encompassing 45.8% and 2.4% of total cultivated land area respectively. Another two pairs exhibited desirable trade-offs (increasing SOC accumulation and decreasing N loss; increasing SOC accumulation and decreasing N₂O emission), accounting for 19.0%, and 2.4% of total cultivated land area respectively. There was considerable overlap within counties, which showed high values of grain yield, N₂O emission, nitrogen loss, and SOC accumulation in the Basin; but values were relatively high in the east and relatively low in the west. Fertilizer use has significant positive correlations with grain yield and SOC accumulation, and it reduces N loss and N₂O emission. Straw return was predicted to raise grain yields and net five-year difference in SOC accumulation and to reduce N loss, but also to increase N₂O emissions. Recommended strategies to reduce N loss and stabilize grain supply in the study area are 1) reducing fertilizer use by 20% in areas where N application was above 490 kg N/ha, and 2) implementing straw return and reducing fertilizer use by 5% for areas where N application ranged between 380 and 490 kg N/ha.

Assessment of the radiation effect of aerosols on maize production in China

With the recent increases in atmosphere aerosol concentration, its impact on agriculture in China is of considerable concern for scientific community. In this study, the effects that aerosols have on radiation and consequently the production of maize in China were investigated from 2002 to 2014 using the AErosol RObotic NETwork (AERONET) data, Second Simulation of a Satellite Signal in the Solar Spectrum radiative transfer (6S) model, and Agricultural Production Systems sIMulator (APSIM) model. Ten stations in the maize planting areas including Beijing, Xianghe, Taihu, Nanjing, Shanghai, Hefei, Baotou, Lanzhou, Qinghaihu, and Xuzhou stations were selected. The results showed that the APSIM-maize model, which was further calibrated, was able to simulate the interactions between maize and the climatic constraints in the maize planting areas of China. Our results indicated that aerosols obviously reduced the amount of solar radiation reaching the surface during the maize growing season in China. We also found that the aerosols have negative effects on both biomass and yield of maize in China at ten stations. The average annual maize biomass during the maize growing season from 2002 to 2014 decreased by 23.70%. The average yield of maize from 2002 to 2014 decreased by 15.10%. However, the influence of aerosol on different varieties of maize varied. We found the aerosols had greater negative impacts on summer maize than on spring maize. For spring maize, the average biomass and yield from 2002 to 2014 decreased by 10.36% and 5.16%, respectively. However, as for the summer maize, the average biomass and yield from 2002 to 2014 were reduced by 19.72% and 20.56%, respectively. Our findings can provide a useful method for estimating the effect of aerosols on crops at the national level, supporting local agricultural production in coping with the ongoing climate change.

From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over Time

Genotype by environment interaction (G×E) for the target trait, e.g. yield, is an emerging property of agricultural systems and results from the interplay between a hierarchy of secondary traits involving the capture and allocation of environmental resources during the growing season. This hierarchy of secondary traits ranges from basic traits that correspond to response mechanisms/sensitivities, to intermediate traits that integrate a larger number of processes over time and therefore show a larger amount of G×E. Traits underlying yield differ in their contribution to adaptation across environmental conditions and have different levels of G×E. Here, we provide a framework to study the performance of genotype to phenotype (G2P) modeling approaches. We generate and analyze response surfaces, or adaptation landscapes, for yield and yield related traits, emphasizing the organization of the traits in a hierarchy and their development and interactions over time. We use the crop growth model APSIM-wheat with genotype-dependent parameters as a tool to simulate non-linear trait responses over time with complex trait dependencies and apply it to wheat crops in Australia. For biological realism, APSIM parameters were given a genetic basis of 300 QTLs sampled from a gamma distribution whose shape and rate parameters were estimated from real wheat data. In the simulations, the hierarchical organization of the traits and their interactions over time cause G×E for yield even when underlying traits do not show G×E. Insight into how G×E arises during growth and development helps to improve the accuracy of phenotype predictions within and across environments and to optimize trial networks. We produced a tangible simulated adaptation landscape for yield that we first investigated for its biological credibility by statistical models for G×E that incorporate genotypic and environmental covariables. Subsequently, the simulated trait data were used to evaluate statistical genotype-to-phenotype models for multiple traits and environments and to characterize relationships between traits over time and across environments, as a way to identify traits that could be useful to select for specific adaptation. Designed appropriately, these types of simulated landscapes might also serve as a basis to train other, more deep learning methodologies in order to transfer such network models to real-world situations.

Combining Crop Growth Modeling and Statistical Genetic Modeling to Evaluate Phenotyping Strategies

Genomic prediction of complex traits, say yield, benefits from including information on correlated component traits. Statistical criteria to decide which yield components to consider in the prediction model include the heritability of the component traits and their genetic correlation with yield. Not all component traits are easy to measure. Therefore, it may be attractive to include proxies to yield components, where these proxies are measured in (high-throughput) phenotyping platforms during the growing season. Using the Agricultural Production Systems Simulator (APSIM)-wheat cropping systems model, we simulated phenotypes for a wheat diversity panel segregating for a set of physiological parameters regulating phenology, biomass partitioning, and the ability to capture environmental resources. The distribution of the additive quantitative trait locus effects regulating the APSIM physiological parameters approximated the same distribution of quantitative trait locus effects on real phenotypic data for yield and heading date. We use the crop growth model APSIM-wheat to simulate phenotypes in three Australian environments with contrasting water deficit patterns. The APSIM output contained the dynamics of biomass and canopy cover, plus yield at the end of the growing season. Each water deficit pattern triggered different adaptive mechanisms and the impact of component traits differed between drought scenarios. We evaluated multiple phenotyping schedules by adding plot and measurement error to the dynamics of biomass and canopy cover. We used these trait dynamics to fit parametric models and P-splines to extract parameters with a larger heritability than the phenotypes at individual time points. We used those parameters in multi-trait prediction models for final yield. The combined use of crop growth models and multi-trait genomic prediction models provides a procedure to assess the efficiency of phenotyping strategies and compare methods to model trait dynamics. It also allows us to quantify the impact of yield components on yield prediction accuracy even in different environment types. In scenarios with mild or no water stress, yield prediction accuracy benefitted from including biomass and green canopy cover parameters. The advantage of the multi-trait model was smaller for the early-drought scenario, due to the reduced correlation between the secondary and the target trait. Therefore, multi-trait genomic prediction models for yield require scenario-specific correlated traits.

Assessing the long-term impact of conservation agriculture on wheat-based systems in Tunisia using APSIM simulations under a climate change context

Several circulation models are forecasting climate changes in the Mediterranean region. Accordingly, it is expected that water scarcity in the region will be higher with drastic shifts of hydrological and erosive watershed responses. In Tunisia, wheat yields have been variable over the years and are lower than the potential yields. In response, the adoption of conservation agriculture (CA), introduced into Tunisia in 1999 to help adaptation to climate change, has resulted in a substantial reduction in agricultural productivity. CA areas increased from 52 ha in 1999 to 14,000 ha in 2015. Using a modelling approach, the present paper evaluates the potential of CA to adapt wheat-based-systems to climate change in Tunisia. The Agricultural Production Systems Simulator (APSIM) model was used to predict the effect of tillage (conventional tillage [CT] vs. zero-tillage [ZT] and soil residue retention [ZT-RR]) on wheat productivity and soil fertility. Two contrasting locations in Tunisia were studied; one semi-arid (Kef) and one sub-humid (Bizerte). Results showed that the sustainable production of durum wheat under climate change conditions in Tunisia is possible through the adoption of CA practices (ZT and ZT-RR) in both sub-humid and semi-arid areas. In fact, mulching (residue retention) is more effective than CT (under semi-arid and sub-humid conditions) in enhancing wheat yield (15%), water use efficiency (18% and 13%) and soil organic carbon accumulation (0.13 t ha^-1 year^-1 and 0.18 t ha^-1 year^-1). It is also more effective for soil resilience - preventing water erosion (1.7 t ha^-1 year^-1 and 4.6 t ha^-1 year^-1 of soil loss). The present study allowed identification of 260,000 ha as priority areas for CA adoption; this represent one-third of the total cereal area in Tunisia. Appropriate evaluation of the benefits of CA on sustainable agricultural intensification would provide more arguments for effectively supporting CA adoption in Tunisia.

Australian wheat production expected to decrease by the late 21st century

Climate change threatens global wheat production and food security, including the wheat industry in Australia. Many studies have examined the impacts of changes in local climate on wheat yield per hectare, but there has been no assessment of changes in land area available for production due to changing climate. It is also unclear how total wheat production would change under future climate when autonomous adaptation options are adopted. We applied species distribution models to investigate future changes in areas climatically suitable for growing wheat in Australia. A crop model was used to assess wheat yield per hectare in these areas. Our results show that there is an overall tendency for a decrease in the areas suitable for growing wheat and a decline in the yield of the northeast Australian wheat belt. This results in reduced national wheat production although future climate change may benefit South Australia and Victoria. These projected outcomes infer that similar wheat-growing regions of the globe might also experience decreases in wheat production. Some cropping adaptation measures increase wheat yield per hectare and provide significant mitigation of the negative effects of climate change on national wheat production by 2041-2060. However, any positive effects will be insufficient to prevent a likely decline in production under a high CO₂ emission scenario by 2081-2100 due to increasing losses in suitable wheat-growing areas. Therefore, additional adaptation strategies along with investment in wheat production are needed to maintain Australian agricultural production and enhance global food security. This scenario analysis provides a foundation towards understanding changes in Australia's wheat cropping systems, which will assist in developing adaptation strategies to mitigate climate change impacts on global wheat production.

Understanding the Impacts of Soil, Climate, and Farming Practices on Soil Organic Carbon Sequestration: A Simulation Study in Australia

Carbon sequestration in agricultural soils has the capacity to mitigate greenhouse gas emissions, as well as to improve soil biological, physical, and chemical properties. The review of literature pertaining to soil organic carbon (SOC) dynamics within Australian grain farming systems does not enable us to conclude on the best farming practices to increase or maintain SOC for a specific combination of soil and climate. This study aimed to further explore the complex interactions of soil, climate, and farming practices on SOC. We undertook a modeling study with the Agricultural Production Systems sIMulator modeling framework, by combining contrasting Australian soils, climates, and farming practices (crop rotations, and management within rotations, such as fertilization, tillage, and residue management) in a factorial design. This design resulted in the transposition of contrasting soils and climates in our simulations, giving soil-climate combinations that do not occur in the study area to help provide insights into the importance of the climate constraints on SOC. We statistically analyzed the model's outputs to determinate the relative contributions of soil parameters, climate, and farming practices on SOC. The initial SOC content had the largest impact on the value of SOC, followed by the climate and the fertilization practices. These factors explained 66, 18, and 15% of SOC variations, respectively, after 80 years of constant farming practices in the simulation. Tillage and stubble management had the lowest impacts on SOC. This study highlighted the possible negative impact on SOC of a chickpea phase in a wheat-chickpea rotation and the potential positive impact of a cover crop in a sub-tropical climate (QLD, Australia) on SOC. It also showed the complexities in managing to achieve increased SOC, while simultaneously aiming to minimize nitrous oxide (N2O) emissions and nitrate leaching in farming systems. The transposition of contrasting soils and climates in our simulations revealed the importance of the climate constraints on SOC.

Effects of changing climate and cultivar on the phenology and yield of winter wheat in the North China Plain

Understanding how changing climate and cultivars influence crop phenology and potential yield is essential for crop adaptation to future climate change. In this study, crop and daily weather data collected from six sites across the North China Plain were used to drive a crop model to analyze the impacts of climate change and cultivar development on the phenology and production of winter wheat from 1981 to 2005. Results showed that both the growth period (GP) and the vegetative growth period (VGP) decreased during the study period, whereas changes in the reproductive growth period (RGP) either increased slightly or had no significant trend. Although new cultivars could prolong the winter wheat phenology (0.3∼3.8 days per decade for GP), climate warming impacts were more significant and mainly accounted for the changes. The harvest index and kernel number per stem weight have significantly increased. Model simulation indicated that the yield of winter wheat exhibited increases (5.0∼19.4%) if new cultivars were applied. Climate change demonstrated a negative effect on winter wheat yield as suggested by the simulation driven by climate data only (-3.3 to -54.8 kg ha(-1) year(-1), except for Lushi). Results of this study also indicated that winter wheat cultivar development can compensate for the negative effects of future climatic change.