The study, conducted by Mathilde Chen, Nicolas Guilpart, and David Makowski, researchers affiliated with Université Paris-Saclay, INRAE, AgroParisTech, and CIRAD, set out to answer a deceptively simple question. If European farmers converted a portion of their cropland to maize-soybean intercrops, how much soybean could the continent produce, and at what cost to its maize output? The answer, according to their quantitative analysis, is that intercropping could push the European Union a long way toward self-sufficiency in both crops simultaneously, while using substantially less land than growing the two crops separately.

The mechanics of intercropping rest on a biological principle that agronomists call complementarity. Soybean, like other legumes, hosts symbiotic bacteria in nodules on its roots that convert atmospheric nitrogen into plant-available forms, a process known as biological nitrogen fixation. Maize, a cereal with high nitrogen demands and a tall, light-hungry canopy, can draw on some of the nitrogen the legume makes available, while its own root architecture explores the soil differently than that of soybean. When the two species are arranged in alternating rows or strips, each can exploit resources, light, water, and nutrients, that the other would otherwise monopolize. The result, documented in numerous field experiments, is often a land equivalent ratio above one, meaning the combined harvest from a mixed plot exceeds what the same area would yield under single-crop cultivation.

Chen and her colleagues translated this agronomic principle into continental-scale scenarios. Assuming a four-year return period for the crops, meaning that the same parcel of land would host the intercrop once every four years, the researchers calculated what would happen if European farmers devoted 6.3, 13.1, or 20.1 million hectares to maize-soybean mixtures. The projections are striking. At the lowest level of adoption, intercropping would produce roughly 9.1 million tonnes of soybean and 28.8 million tonnes of maize, lifting the European Union to about 25 percent soybean self-sufficiency and 34 percent maize self-sufficiency. At the intermediate level, production would double to 18.2 million tonnes of soybean and 63.7 million tonnes of maize, reaching 50 percent and 75 percent self-sufficiency respectively. And at the highest scenario, 20.1 million hectares of intercropping would yield 27.2 million tonnes of soybean and 99.0 million tonnes of maize, achieving 75 percent soybean self-sufficiency and, remarkably, 116 percent maize self-sufficiency, meaning Europe would produce more maize than it needs.

What makes these numbers genuinely compelling is the comparison with the alternative. The researchers calculated how much additional land would be needed to produce the same combined quantities of soybean and maize using sole crops, the monocultures that dominate European agriculture today. The answer: 11.1, 24.7, and 36.5 million hectares for the three scenarios respectively. In other words, intercropping delivers a land saving of roughly 20 to 21 percent. On a continent where agricultural land is under competing pressure from rewilding initiatives, urban expansion, and bioenergy production, a fifth of the land requirement simply vanishing is not a marginal gain. It is the difference between a feasible transition and an impossible one.

The study also confronts the limits of the strategy with unusual candor. Full soybean self-sufficiency, producing every tonne the European Union consumes domestically, would require more than 25 million hectares of intercrops annually, equivalent to about 23 percent of all European cropland. That is an enormous share of the continent’s farmland, and the authors do not pretend otherwise. Devoting nearly a quarter of European cropland to a single intercropping system would have profound implications for crop rotation diversity, livestock feed balances, and the many other crops, from wheat to oilseed rape, that currently occupy that land. The scenarios therefore represent a potential ceiling, not a policy prescription, and the trade-offs with other crops’ production that the researchers explicitly sought to minimize would need careful management in any real-world deployment.

One of the most technically interesting aspects of the analysis is its treatment of uncertainty. The researchers tested their estimates across a wide range of nitrogen input levels and different degrees of temporal overlap between the two species, the window during which both crops grow simultaneously in the field. The projections held robust under this broad parameter space, with one critical condition: one hectare of intercropping must produce more soybean than half a hectare of sole soybean. This threshold captures the essential economics of the system. If the legume’s share of the mixed harvest falls too low, the land saved by intercropping evaporates, and the practice loses its advantage over simply planting the two crops side by side in separate fields. The finding underscores that the design of the intercrop, the row arrangement, the relative densities, and the sowing dates, is not a detail but the decisive variable.

Equally important is the study’s methodological warning about how land-use efficiency should be assessed. The authors show that geographic variation in sole crop yields must be factored into any evaluation of intercropping performance. A hectare of soybean in northern France does not produce what a hectare of soybean produces in Romania, and maize yields vary just as dramatically across Europe’s climatic gradients. Comparisons that ignore this spatial heterogeneity can systematically overstate or understate the benefits of mixing crops. By anchoring their analysis in regionally differentiated yield data, Chen and her colleagues produced estimates that reflect the actual mosaic of European agriculture rather than a flat, idealized average, and they argue that future assessments of intercropping elsewhere should follow the same logic.

The broader context makes the timing of this research particularly significant. The European Union has repeatedly pledged to reduce its reliance on imported feed proteins, and previous data-driven projections have suggested that climate change could open new opportunities for soybean cultivation at Europe’s northern latitudes. Yet expanding soybean as a sole crop means competing directly with other land uses, whereas intercropping threads the needle by co-producing two staple commodities on the same hectares. The nitrogen-fixing legume also reduces the fertilizer burden of the associated cereal, a meaningful consideration as European agriculture faces tightening regulations on nutrient losses and rising input costs. In this light, the study’s finding that intercrops could satisfy all of Europe’s maize needs at the highest adoption scenario while simultaneously covering three-quarters of its soybean demand reads as an invitation to take mixed cropping far more seriously in agricultural policy.

None of this implies that the transition would be simple. Intercropping demands more sophisticated machinery for sowing and harvesting two species with different grain types, supply chains calibrated to mixed outputs, and agronomic knowledge that decades of monoculture-focused research have allowed to atrophy in Europe. Yields in farmers’ fields often fall short of experimental plots, and the four-year return period assumed in the study embeds rotation constraints that vary across member states. But the quantitative message of the new analysis is difficult to dismiss. By rigorously mapping the trade-offs, the land savings, and the conditions under which maize-soybean intercropping pays off, Chen, Guilpart, and Makowski have provided the clearest continental-scale evidence yet that the oldest form of agricultural diversification could help solve one of Europe’s most modern dilemmas: feeding its livestock and its people without exporting its environmental footprint across the Atlantic.

Subject of Research: Maize-soybean intercropping as a pathway to European soybean and maize self-sufficiency

Article Title: High potential contribution of intercropping to soybean and maize self-sufficiency in Europe

Article References: Chen, M., Guilpart, N., & Makowski, D. (2026). High potential contribution of intercropping to soybean and maize self-sufficiency in Europe. Nature Communications. https://doi.org/10.1038/s41467-026-78206-4

Image Credits: AI Generated

DOI: 10.1038/s41467-026-78206-4

Keywords: intercropping, soybean, maize, self-sufficiency, European Union, land use, nitrogen fixation, agroecology, crop rotation, food security, agricultural modeling, Nature Communications

The study, conducted by Hüdaverdi Gürkan of the University of Florida, the Turkish State Meteorological Service, and Adana Alparslan Turkes Science and Technology University, together with Gerrit Hoogenboom of the University of Florida’s Global Food Systems Institute, set out to answer a deceptively simple question: when NASA POWER says it rained, or reports yesterday’s maximum temperature, how closely does that match what actually happened on the ground? The answer, the researchers found, depends heavily on which variable you care about and where the point of interest sits on the map. For temperature and solar radiation, the news is overwhelmingly positive. For humidity, wind, and precipitation, geography matters enormously, and the dataset’s reliability can degrade sharply along coastlines and in mountainous terrain.

The statistical evidence for temperature is striking. Maximum daily temperature records from NASA POWER explained between 87 and 98 percent of the variance in ground observations, with root mean square errors ranging from 1.50 to 6.89 degrees Celsius. Minimum temperatures performed nearly as well, with R-squared values between 0.78 and 0.97 and errors from 1.23 to 8.37 degrees Celsius. Solar radiation estimates tracked ground measurements with R-squared values from 0.76 to 0.97 and root mean square errors between 1.73 and 3.84 megajoules per square meter. Across all temperature and radiation variables, the study reports overall R-squared values between 0.76 and 0.98, a level of agreement that the authors say establishes NASA POWER as a dependable foundation for climate modeling and agricultural decision support in most settings.

The implications for crop modeling are significant. Crop simulation models, which are used worldwide to forecast yields, evaluate irrigation strategies, and assess climate change impacts, are notoriously sensitive to input weather data. Small biases in daily minimum temperature can alter calculations of growing degree days and chilling requirements, while errors in solar radiation propagate directly into photosynthesis and evapotranspiration estimates. Previous work by Hoogenboom and colleagues, including earlier evaluations of NASA satellite-derived temperature and radiation data over the continental United States, laid the groundwork for using such datasets in agricultural applications. The new study extends that legacy by testing the modern POWER product across a far broader range of topographic and climatic settings, from the humid subtropics of Florida to the complex mountain terrain of Türkiye.

That topographic diversity is precisely where the study’s cautionary findings emerge. The researchers found that the dataset’s performance for relative humidity, wind speed, and precipitation is strongly controlled by local geography. In coastal areas, land-sea breeze circulations and marine atmospheric layers introduce discrepancies that the gridded satellite and reanalysis data struggle to capture. NASA POWER produces values on a grid, essentially averaging conditions over cells of finite size, and these spatial averages smooth over the sharp gradients that occur where land meets ocean. A weather station a few hundred meters from the shoreline can experience humidity and wind regimes that differ substantially from the grid-cell average, producing errors that would surprise a user relying on the dataset without checking it first.

High-relief terrain presents a parallel problem that atmospheric scientists call topographic smoothing. When elevation changes dramatically within a single grid cell, as it does across the mountain ranges of Anatolia or the slopes of Washington State’s Cascades, a single gridded value cannot represent the true spread of conditions from valley floor to ridge crest. The dataset’s estimates regress toward the mean of the cell, underestimating the extremes that stations actually record. This phenomenon, well documented in studies of gridded precipitation in complex terrain, means that hydrologists and agronomists working in mountainous regions should treat NASA POWER outputs for precipitation, humidity, and wind with particular skepticism, or apply bias correction before feeding them into models.

The practical value of the study lies in the regional performance thresholds it establishes. Rather than issuing a blanket verdict on the dataset, the authors provide what amounts to a decision guide: researchers can look up their variable of interest and their terrain type, and determine in advance whether raw NASA POWER data is adequate or whether bias correction is necessary. This distinction matters because bias correction is not free. It requires access to local observations, adds processing steps, and introduces its own uncertainties. Knowing that temperature and radiation can typically be used as-is, while coastal precipitation may demand correction, saves both computational effort and scientific risk. In an era when open-access geospatial datasets are increasingly substituted for sparse station networks, such interoperability benchmarks become essential infrastructure for environmental monitoring.

The validation design itself reflects a growing awareness that dataset quality assessments must span decades, not just single seasons. By drawing on records of 15 to 43 years from networks including the Florida Automated Weather Network, the Georgia Automated Environmental Monitoring Network, Washington State University’s AgWeatherNet, and the Turkish State Meteorological Service, the study captures the full range of interannual variability, El Niño and La Niña cycles, heat waves, and drought years that any operational dataset must handle. A dataset that performs well only in average years is of limited use for the extreme events that matter most to farmers and water managers. The multi-decadal approach also guards against the possibility of agreement arising by coincidence in a short window of unusually stable weather.

The research also situates NASA POWER within a crowded field of gridded climate products, including ERA5, MERRA-2, CRU TS, and CHIRPS, each with its own strengths and known weaknesses. What distinguishes POWER is its accessibility: it is free, requires no registration, and delivers daily agroclimatic variables in formats that plug directly into widely used crop models. The authors’ benchmark gives the global community of agricultural modelers, many of whom work in data-sparse regions of Africa, Central Asia, and South America, a defensible basis for choosing POWER, and a clear-eyed understanding of where it will need help. As climate change intensifies the demand for reliable weather information in agriculture, studies like this one transform a free dataset from a convenient substitute into a rigorously characterized scientific instrument.

Subject of Research: Multi-decadal validation of the NASA POWER gridded meteorological dataset against ground weather stations in the United States and Türkiye for climatology and agricultural modeling applications

Article Title: Validating NASA POWER meteorological data for applied climatology and agricultural modeling: a multi-decadal assessment across diverse topographies in the US and Türkiye

Article References: Gürkan, H., & Hoogenboom, G. (2026). Validating NASA POWER meteorological data for applied climatology and agricultural modeling: a multi-decadal assessment across diverse topographies in the US and Türkiye. Theoretical and Applied Climatology, 157(10), Article 621. https://doi.org/10.1007/s00704-026-06532-0

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06532-0

Keywords: NASA POWER, gridded meteorological data, weather station validation, applied climatology, agricultural modeling, crop simulation, solar radiation, temperature data, precipitation accuracy, topographic effects, bias correction, Türkiye