Data-driven projections suggest large opportunities to improve Europe's soybean self-sufficiency under climate change

An assessment of the relationship between spring frost indicators and global crop yield losses

Reports on the influences of spring frost on crop losses are not consistent, which may be because insufficient indicators of spring frost were included in the analysis. To bridge this gap, we analyzed global temperature datasets and production data for the three major crops of maize, winter wheat, and rice from 1981 to 2016. Five indicators of spring frost events: temperature fluctuation (Tv), temperature difference (Td), duration (Thour), occurrence date (Tdate), and frequency (Tnum) were considered to assess their relationship with yield losses. Linear regression was employed to analyze the change trends in five indicators and random forest was utilized to investigate the relationship between yield loss and indicators of spring frost. Our findings reveal that, despite a decline in the number of spring frost events during global warming, not all the five indicators declined over time. Tv is the most important indicator for yield losses in maize and winter wheat, which shows an increasing trend in their growing regions and provides an explanation for the increasing yield losses of maize and winter wheat over time. Td is the most important indicator of rice yield losses but it shows a decreasing trend in rice-growing areas, which explains why rice yield losses from spring frosts in recent years are not significant.

European soybean to benefit people and the environment

Europe imports large amounts of soybean that are predominantly used for livestock feed, mainly sourced from Brazil, USA and Argentina. In addition, the demand for GM-free soybean for human consumption is project to increase. Soybean has higher protein quality and digestibility than other legumes, along with high concentrations of isoflavones, phytosterols and minerals that enhance the nutritional value as a human food ingredient. Here, we examine the potential to increase soybean production across Europe for livestock feed and direct human consumption, and review possible effects on the environment and human health. Simulations and field data indicate rainfed soybean yields of 3.1 ± 1.2 t ha-1 from southern UK through to southern Europe (compared to a 3.5 t ha-1 average from North America). Drought-prone southern regions and cooler northern regions require breeding to incorporate stress-tolerance traits. Literature synthesized in this work evidenced soybean properties important to human nutrition, health, and traits related to food processing compared to alternative protein sources. While acknowledging the uncertainties inherent in any modelling exercise, our findings suggest that further integrating soybean into European agriculture could reduce GHG emissions by 37-291 Mt CO2e year-1 and fertiliser N use by 0.6-1.2 Mt year-1, concurrently improving human health and nutrition.

Extreme rainfall erosivity: Research advances and future perspectives

Extreme rainfall erosivity, the capacity of intense rainfall to induce soil erosion, is vital for anticipating future impacts on soil conservation. Despite extensive research, significant differences persist in terms of understanding influencing mechanisms, potential impacts, estimation models and future trends of extreme rainfall erosivity. Quantitatively describing extreme rainfall erosivity remains a key issue in existing research. In this study, we comprehensively reviewed the literature to assess the relationships between extreme rainfall characteristics and rainfall erosivity, between extreme rainfall erosivity and soil erosion, estimation models and trend prediction. The aim was to summarize previous related research and achievements, providing a better understanding of the generation, impacts and future trends of extreme rainfall erosivity. Future research directions should include identifying the thresholds of extreme rainfall events, increasing research attention on tropical cyclones in terms of rainfall erosivity, considering on the impact of extreme rainfall erosivity on soil erosion, and improving rainfall erosivity estimation and simulation prediction methods. This study could contribute to adapting to global climate change and aiding in formulating soil erosion prevention and environmental protection recommendations.

Mitigating nitrogen losses with almost no crop yield penalty during extremely wet years

Climate change-induced precipitation anomalies during extremely wet years (EWYs) result in substantial nitrogen losses to aquatic ecosystems (Nw). Still, the extent and drivers of these losses, and effective mitigation strategies have remained unclear. By integrating global datasets with well-established crop modeling and machine learning techniques, we reveal notable increases in Nw, ranging from 22 to 56%, during historical EWYs. These pulses are projected to amplify under the SSP126 (SSP370) scenario to 29 to 80% (61 to 120%) due to the projected increases in EWYs and higher nitrogen input. We identify the relative precipitation difference between two consecutive years (diffPr) as the primary driver of extreme Nw. This finding forms the basis of the CLimate Extreme Adaptive Nitrogen Strategy (CLEANS), which scales down nitrogen input adaptively to diffPr, leading to a substantial reduction in extreme Nw with nearly zero yield penalty. Our results have important implications for global environmental sustainability and while safeguarding food security.

Crop production on the Chinese Loess Plateau under 1.5 and 2.0 °C global warming scenarios

Global warming is a crucial factor affecting crop production in ecologically vulnerable areas. Warming-induced changes in the yields of different crops could pose significant challenges to food security and sustainability assessment. In this study, the World Food Studies model and a remote sensing product assimilation algorithm were used to develop a spatially explicit crop assimilation model applicable to the Loess Plateau of China. The model was used to simulate potential changes in actual yields and yield gaps for winter wheat and maize under three typical climate scenarios (Representative Concentration Pathways (RCPs): RCP 2.6, RCP 4.5, and RCP 8.5) from 2016 to 2060. Average yields increased in both winter wheat (2.38 %-4.96 %) and maize (5.41 %-6.85 %), with maize (RCP 4.5 > RCP 8.5 > RCP 2.6) more adapted to climate warming than winter wheat (RCP 2.6 > RCP 8.5 > RCP 4.5) in terms of yield increase rate. The yield increase and yield gap for winter wheat decreased most significantly in RCP2.6 (-2.28 %). Maize yield did not exceed 80 % of the potential yield in any scenario. The average phenological periods for winter wheat and maize are predicted be 2-4 and 9-16 days earlier, respectively. Crop yields were negatively correlated with radiation and yield gaps were positively correlated with precipitation. Future climate change will likely cause dramatic interannual crop yield fluctuations. Winter wheat is predicted to experience yield stagnation after 2050, whereas maize production potential will increase briefly before experiencing a long-term decline in growth. The results of this multi-scenario simulation assessment of crop production provide scientific support for implementing climate-adapted crop management strategies and integrated dry-crop-irrigated agriculture to meet food security objectives in this ecologically fragile area. We recommend integrated management measures to ensure regional food security through crop variety improvement, irrigation regulation, and planting structure optimization.

An experimental dataset on yields of pulses across Europe

Future European agriculture should achieve high productivity while limiting its impact on the environment. Legume-supported crop rotations could contribute to these goals, as they request less nitrogen (N) fertilizer inputs, show high resource use efficiency and support biodiversity. However, legumes grown for their grain (pulses) are not widely cultivated in Europe. To further expand their cultivation, it remains crucial to better understand how different cropping and environmental features affect pulses production in Europe. To address this gap, we collected the grain yields of the most cultivated legumes across European countries, from both published scientific papers and unpublished experiments of the European projects LegValue and Legato. Data were integrated into an open-source, easily updatable dataset, including 5229 yield observations for five major pulses: chickpea (Cicer arietinum L.), faba bean (Vicia faba L.), field pea (Pisum sativum L.), lentil (Lens culinaris Medik.), and soybean (Glycine max (L.) Merr.). These data were collected in 177 field experiments across 21 countries, from 37° N (southern Italy) to 63° N (Finland) of latitude, and from ca. 8° W (western Spain) to 47° E (Turkey), between 1980 and 2020. Our dataset can be used to quantify the effects of the soil, climate, and agronomic factors affecting pulses yields in Europe and could contribute to identifying the most suitable cropping areas in Europe to grow pulses.

Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy

Context

Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency.

Objective

The aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers' fields in contrasting farming systems worldwide.

Methods

A large database of 10,940 field-year combinations from three countries in different stages of agricultural intensification was analyzed. Random effects models were used to partition crop yield variability and random forest models were used to explain and predict crop yield within a cross-validation scheme with data re-sampling over space and time.

Results

Yield variability in relative terms was smallest for wheat and barley in the Netherlands and for wheat in Ethiopia, intermediate for rice in the Philippines, and greatest for maize in Ethiopia. Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. Crop management related variables were important to explain and predict cereal yields in Ethiopia, while predictive (i.e., known before the growing season) climatic variables and explanatory (i.e., known during or after the growing season) climatic variables were most important to explain and predict cereal yield variability in the Philippines and in the Netherlands, respectively. Finally, model cross-validation for regions or years not seen during model training reduced the R2 considerably for most crop x country combinations, while for wheat in the Netherlands this was model dependent.

Conclusion

Big data from farmers' fields is useful to explain on-farm yield variability to some extent, but not to predict it across time and space.

Significance

The results call for moderate expectations towards big data and machine learning in agronomic studies, particularly for smallholder farms in the tropics where model performance was poorest independently of the variables considered and the cross-validation scheme used.

China can be self-sufficient in maize production by 2030 with optimal crop management

Population growth and economic development in China has increased the demand for food and animal feed, raising questions regarding China's future maize production self-sufficiency. Here, we address this challenge by combining data-driven projections with a machine learning method on data from 402 stations, with data from 87 field experiments across China. Current maize yield would be roughly doubled with the implementation of optimal planting density and management. In the 2030 s, we estimate a 52% yield improvement through dense planting and soil improvement under a high-end climate forcing Shared Socio-Economic Pathway (SSP585), compared with a historical climate trend. Based on our results, yield gains from soil improvement outweigh the adverse effects of climate change. This implies that China can be self-sufficient in maize by using current cropping areas. Our results challenge the view of yield stagnation in most global areas and provide an example of how food security can be achieved with optimal crop-soil management under future climate change scenarios.

It takes three to tango: citizen, fundamental and applied science

Citizen science is an undervalued tool in a scientist's toolbox with the potential to go beyond primary data collection to strengthen fundamental and applied science. We call for the integration of these three disciplines to make agriculture sustainable and adaptive to climate change, with North-Western European soybean cultivation as showcase.

Future area expansion outweighs increasing drought risk for soybean in Europe

The European Union is highly dependent on soybean imports from overseas to meet its protein demands. Individual Member States have been quick to declare self-sufficiency targets for plant-based proteins, but detailed strategies are still lacking. Rising global temperatures have painted an image of a bright future for soybean production in Europe, but emerging climatic risks such as drought have so far not been included in any of those outlooks. Here, we present simulations of future soybean production and the most prominent risk factors across Europe using an ensemble of climate and soybean growth models. Projections suggest a substantial increase in potential soybean production area and productivity in Central Europe, while southern European production would become increasingly dependent on supplementary irrigation. Average productivity would rise by 8.3% (RCP 4.5) to 8.7% (RCP 8.5) as a result of improved growing conditions (plant physiology benefiting from rising temperature and CO₂ levels) and farmers adapting to them by using cultivars with longer phenological cycles. Suitable production area would rise by 31.4% (RCP 4.5) to 37.7% (RCP 8.5) by the mid-century, contributing considerably more than productivity increase to the production potential for closing the protein gap in Europe. While wet conditions at harvest and incidental cold spells are the current key challenges for extending soybean production, the models and climate data analysis anticipate that drought and heat will become the dominant limitations in the future. Breeding for heat-tolerant and water-efficient genotypes is needed to further improve soybean adaptation to changing climatic conditions.

The Potential Role of Iceland in Northern Europe's Protein Self-Sufficiency: Feasibility Study of Large-Scale Production of Spirulina in a Novel Energy-Food System

Europe is dependent on protein-rich crop imports to meet domestic food demand. This has moved the topic of sustainable protein self-sufficiency up the policy agenda. The current study assesses the feasibility of protein self-sufficiency in Iceland, and its capacity to meet Northern Europe's demand, based on industrial-scale cultivation of Spirulina in novel production units. Production units currently operating in Iceland, and laboratory-derived nutritional profile for the Spirulina cultivated, provide the basis for a theoretical protein self-sufficiency model. Integrating installed and potentially installed energy generation data, the model elaborates six production scale-up scenarios. Annual biomass produced is compared with recommended dietary allowance figures for protein and essential amino acids to determine whether Northern Europe's population demands can be met in 2030. Results show that Iceland could be protein self-sufficient under the most conservative scenario, with 20,925 tonnes of Spirulina produced using 15% of currently installed capacity. In a greater allocation of energy capacity used by heavy industry, Iceland could additionally meet the needs of Lithuania, or Latvia, Estonia, Jersey, Isle of Man, Guernsey, and Faroe Islands. Under the most ambitious scenario utilizing planned energy projects, Iceland could support itself plus Denmark, or Finland, or Norway, or Ireland with up to 242,366 tonnes of biomass. On a protein-per-protein basis, each kilogram of Spirulina consumed instead of beef could save 0.315 tonnes CO2-eq. Under the most ambitious scenario, this yields annual savings of 75.1 million tonnes CO2-eq or 7.3% of quarterly European greenhouse gas emissions. Finally, practicalities of production scale-up are discussed.

Fermented Soy Products and Their Potential Health Benefits: A Review

In the growing search for therapeutic strategies, there is an interest in foods containing natural antioxidants and other bioactive compounds capable of preventing or reversing pathogenic processes associated with metabolic disease. Fermentation has been used as a potent way of improving the properties of soybean and their components. Microbial metabolism is responsible for producing the β-glucosidase enzyme that converts glycosidic isoflavones into aglycones with higher biological activity in fermented soy products, in addition to several end-metabolites associated with human health development, including peptides, phenolic acids, fatty acids, vitamins, flavonoids, minerals, and organic acids. Thus, several products have emerged from soybean fermentation by fungi, bacteria, or a combination of both. This review covers the key biological characteristics of soy and fermented soy products, including natto, miso, tofu, douchi, sufu, cheonggukjang, doenjang, kanjang, meju, tempeh, thua-nao, kinema, hawaijar, and tungrymbai. The inclusion of these foods in the diet has been associated with the reduction of chronic diseases, with potential anticancer, anti-obesity, antidiabetic, anticholesterol, anti-inflammatory, and neuroprotective effects. These biological activities and the recently studied potential of fermented soybean molecules against SARS-CoV-2 are discussed. Finally, a patent landscape is presented to provide the state-of-the-art of the transfer of knowledge from the scientific sphere to the industrial application.