The Analysis of Wheat Yield Variability Based on Experimental Data from 2008–2018 to Understand the Yield Gap

Among European countries, Poland has the largest gap in the grain yield of winter wheat, and thus the greatest potential to reduce this yield gap. This paper aims to recognize the main reasons for winter wheat yield variability and shed the light on possible reasons for this gap. We used long-term datasets (2008–2018) from individual commercial farms obtained by the Laboratory of Economics of Seed and Plant Breeding of Plant Breeding and Acclimatization Institute (IHAR)-National Research Institute (Poland) and the experimental fields with high, close to potential yield, in the Polish Post-Registration Variety Testing System in multi-environmental trials. We took into account environment, management and genetic variables. Environment was considered through soil class representing soil fertility. For the crop management, the rates of mineral fertilization, the use of pesticides and the type of pre-crop were considered. Genotype was represented by the independent variable year of cultivar registration or year of starting its cultivation in Poland. The analysis was performed using the CART (Classification and Regression Trees). The winter wheat yield variability was mostly dependent on the amount of nitrogen fertilization applied, soil quality, and type of pre-crop. Genetic variable was also important, which means that plant breeding has successfully increased genetic yield potential especially during the last several years. In general, changes to management practices are needed to lower the variability of winter wheat yield and possibly to close the yield gap in Poland.

Impacts of compound hot–dry extremes on US soybean yields

The US agriculture system supplies more than one-third of globally traded soybean, and with 90 % of US soybean produced under rainfed agriculture, soybean trade is particularly sensitive to weather and climate variability. Average growing season climate conditions can explain about one-third of US soybean yield variability. Additionally, crops can be sensitive to specific short-term weather extremes, occurring in isolation or compounding at key moments throughout crop development. Here, we identify the dominant within-season climate drivers that can explain soybean yield variability in the US, and we explore the synergistic effects between drivers that can lead to severe impacts. The study combines weather data from reanalysis and satellite-informed root zone soil moisture fields with subnational crop yields using statistical methods that account for interaction effects. On average, our models can explain about two-thirds of the year-to-year yield variability (70 % for all years and 60 % for out-of-sample predictions). The largest negative influence on soybean yields is driven by high temperature and low soil moisture during the summer crop reproductive period. Moreover, due to synergistic effects, heat is considerably more damaging to soybean crops during dry conditions and is less problematic during wet conditions. Compounding and interacting hot and dry (hot–dry) summer conditions (defined by the 95th and 5th percentiles of temperature and soil moisture respectively) reduce yields by 2 standard deviations. This sensitivity is 4 and 3 times larger than the sensitivity to hot or dry conditions alone respectively. Other relevant drivers of negative yield responses are lower temperatures early and late in the season, excessive precipitation in the early season, and dry conditions in the late season. We note that the sensitivity to the identified drivers varies across the spatial domain. Higher latitudes, and thus colder regions, are positively affected by high temperatures during the summer period. On the other hand, warmer southeastern regions are positively affected by low temperatures during the late season. Historic trends in identified drivers indicate that US soybean production has generally benefited from recent shifts in weather except for increasing rainfall in the early season. Overall, warming conditions have reduced the risk of frost in the early and late seasons and have potentially allowed for earlier sowing dates. More importantly, summers have been getting cooler and wetter over the eastern US. Nevertheless, despite these positive changes, we show that the frequency of compound hot–dry summer events has remained unchanged over the 1946–2016 period. In the longer term, climate models project substantially warmer summers for the continental US, although uncertainty remains as to whether this will be accompanied by drier conditions. This highlights a critical element to explore in future studies focused on US agricultural production risk under climate change.

Identifying Factors Explaining Yield Variability of Miscanthus x giganteus and Miscanthus sinensis Across Contrasting Environments: Use of an Agronomic Diagnosis Approach

Miscanthus is a perennial C4 crop whose lignocellulose can be used as an alternative to the production of biosourced material. Miscanthus x giganteus (M. x giganteus) has demonstrated high maximum yields but also high yield variability across farmers’ fields. Miscanthus sinensis (M. sinensis) can be an alternative to M. x giganteus because it is considered to be more tolerant to water stress and to produce more stable yields. This study aimed to identify the main factors explaining the variability of yields across site-years for M. x giganteus and M. sinensis. A multi-local and multi-year trial network was set up in France (Ile de France and Center regions). Four treatments were established on seven sites, from spring 2013 to winter 2019: at each site, two treatments of M. x giganteus (a treatment from rhizome and a treatment from rhizome-derived plantlets) and two treatments of M. sinensis (a treatment from seed-derived plantlets established in single density and a treatment from seed-derived plantlets established in double density). We experienced 5 years of harvest because miscanthus was not harvested in 2014. First, we characterized yield variations across site-years for both genotypes. Second, we defined and calculated a set of indicators (e.g., water stress indicator, sum of degree-days of the previous year, number of frost days) that could affect miscanthus yields. Finally, we performed a mixed model with re-sampling to identify the main indicators that explained yield variability for each genotype specifically. Results showed that water stress and crop age mainly explained yield variability for both genotypes. M. sinensis yields were also affected by the sum of degree-days of the previous year of growth. Hence, genotype choice must take into account environmental characteristics. M. sinensis could indeed achieve higher and more stable yields than those of M. x giganteus in shallow sandy soils or in locations with a higher risk of low rainfall.