Evidence for increasing global wheat yield potential

Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 +/- 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges

Identification of technology options for reducing nitrogen pollution in cropping systems of Pujiang

This work analyses the potential role of nitrogen pollution technology of crop systems of Pujiang, County in Eastern China’s Zhejiang Province, rice and vegetables are important cropping systems. We used a case study approach involving comparison of farmer practices and improved technologies. This approach allows assessing the impact of technology on pollution, is forward looking, and can yield information on the potential of on-the-shelf technology and provide opportunities for technology development. The approach particularly suits newly developed rice technologies with large potential of reducing nitrogen pollution and for future rice and vegetables technologies. The results showed that substantial reductions in nitrogen pollution are feasible for both types of crops

Reversal of Rice Yield Decline in a Long-Term Continuous Cropping Experiment

In a long-term continuous cropping experiment at Los Baños, Philippines, three rice (Oryza sativa L.) crops were grown each year with the goals of maximum annual grain production and high N use efficiency. Our objective was to identify the factors responsible for the restoration of yields occurring after 1991. From 1968 to 1991, grain yields declined at an annual rate of 1.4 to 2.0%. From 1991 to 1995, dry season (DS) yields in the highest N treatment increased to within 80 to 100% of the simulated yield potential; yields in the unfertilized control did not increase. Increased solar radiation, increased N rate, and improved timing of N applications accounted for the restoration of yields in the DS. Wet season yields increased in fertilized and unfertilized plots due to greater solar radiation, improved timing of N applications, and increased soil N supply due to dry fallow periods in three years. Residual benefits of soil aeration were short-term. Reducing preplant N fertilizer and increasing y the number of split applications had a greater effect on increasing yield than the increase in the amount of N applied. Our results provide evidence that N deficiency caused the yield decline before 1991. However, the actual processes that caused a decline in soil N supply or plant uptake remain to be determined. It is possible to sustain high yields and high N use efficiency if fertilizer regimes are updated regularly to maintain the congruence between crop N demand and the N supply from soil and fertilizer

Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables

International audienceWe assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Delta), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the., especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area