The research team, led by Priscila Belen Cano of Purdue University’s Department of Agronomy together with Gustavo Angel Maddonni of the Universidad de Buenos Aires and Ignacio Antonio Ciampitti, developed what they call a yield-based environmental regionalization framework. Rather than starting with climate maps or soil surveys, the researchers began with the crop itself. They compiled median annual maize yields for each of Indiana’s 92 counties from 2000 to 2024, drawing on insured production records from the United States Department of Agriculture Risk Management Agency. Because long-term yield integrates the cumulative effects of climate, soils, topography, and management, spatially persistent differences in productivity can be read as realized environmental differences, the authors explain.

To transform those yield patterns into coherent geographic zones, the team applied a fuzzy c-means clustering algorithm, a machine learning technique that assigns each county a degree of membership in multiple clusters rather than forcing hard boundaries. The feature matrix combined each county’s median yield with the latitude and longitude of its geographic centroid, with the coordinates weighted by a spatial factor of 1.5 to encourage geographic contiguity while preserving yield homogeneity. Selecting the optimal number of clusters required a multi-criteria evaluation using the Xie–Beni index, the Fukuyama–Sugeno index, and the Fuzzy Partition Coefficient. Because these indices favored different solutions, the researchers settled on five clusters as a balanced compromise between partition quality, model complexity, and agronomic interpretability. The result was five yield-based environmental regions spanning the state: northwest, northeast, west-central, southwest, and southeast.

The regionalization exposed a striking productivity gradient. The west-central region emerged as Indiana’s maize powerhouse, with median yields of approximately 11.2 megagrams per hectare, while the northwest region lagged at roughly 8.5 megagrams per hectare, accompanied by the greatest spatial dispersion in county yields, with a standard deviation of 2.6 megagrams per hectare compared to less than 0.7 in the other regions. The northeast, southwest, and southeast occupied intermediate positions with relatively similar median yields. Crucially, climate variables were deliberately excluded from the clustering procedure so they could later serve as an independent test of whether the yield-defined zones corresponded to genuinely distinct climatic environments.

That test came in the form of a principal component analysis of long-term climatic means, built from the Daymet gridded meteorological dataset at one-kilometer resolution. The first two principal components together explained nearly 84 percent of the total variance, and the dominant variables separating the regions were vapor pressure deficit, reference evapotranspiration, and a heat-stress index capturing cumulative degrees of daily maximum temperatures above 35 degrees Celsius. The first component discriminated between environments with longer growing seasons, higher atmospheric demand, and greater heat stress, exemplified by the southwest, and cooler environments with shorter frost-free periods and more grain-filling precipitation, exemplified by the northeast and northwest. The second component captured contrasts in diurnal temperature amplitude and solar radiation, particularly during the critical period, cleanly separating the northeast from the northwest and the southwest from the southeast.

Yields rose everywhere over the study period, but not at the same relative pace. Absolute gains ranged narrowly from 0.172 to 0.185 megagrams per hectare per year, yet in relative terms the lower-yielding northwest improved at about 2 percent per year while the high-yielding west-central region gained about 1.6 percent annually. This pattern suggests a tendency toward relative convergence in maize performance across the state, even though absolute yield gaps between regions may persist. Interannual variability also differed, with coefficients of variation around 16 to 18 percent in the higher-yielding regions and approximately 21 percent in the southern zones, hinting that southern Indiana farmers face a rockier road to consistent harvests.

To isolate the climatic drivers of year-to-year swings, the researchers first removed long-term technological trends by fitting linear regressions of yield against year for each county, retaining the residuals as a measure of pure interannual variability. They then correlated these residuals with climatic anomalies, defined as the deviation of each county-year observation from the county’s 2000 to 2024 long-term mean for each variable. The results were unambiguous. During the vegetative period, relationships were weaker and more region-dependent, though elevated vapor pressure deficit and heat-stress indices consistently tracked negative yield residuals. During grain filling, higher solar radiation anomalies were reliably beneficial. But it was the critical period, defined as fifteen days before to fifteen days after silking, that exhibited the strongest and most consistent relationships across all five regions.

Vapor pressure deficit around flowering showed the most powerful association with interannual yield variability in the northeast, southeast, and southwest regions, with correlation coefficients ranging from minus 0.44 in the northwest to minus 0.78 in the southeast. Slope values were equally telling, spanning from minus 3.24 megagrams per hectare per unit of VPD anomaly in the northwest to minus 9.53 in the southeast. In the northwest and west-central regions, the heat-stress index during the same window took precedence, though with somewhat weaker relationships, a pattern the authors attribute to the more continental thermal regime of western Indiana, where episodic extreme heat exerts a proportionally larger influence than background evaporative demand. Across every region, the worst yield outcomes clustered where high VPD and extreme heat anomalies coincided during pollination.

The physiological story behind these statistics is well grounded. Maize is most vulnerable around silking because this stage determines kernel number per plant, the foundation of final yield. Elevated vapor pressure deficit during this window reduces pollen viability, accelerates silk desiccation, and impairs pollination success, leading directly to kernel set failure. In parallel, high atmospheric demand intensifies plant water stress and triggers stomatal closure, cutting carbon assimilation and increasing kernel abortion. Extreme temperatures above 35 degrees Celsius compound these effects, while heat during grain filling reduces kernel weight and quality. The authors caution that because many of the evaluated variables are physically interrelated, their correlation analysis identifies the variables most strongly associated with yield variability rather than proving independent causal effects, and that disentangling those contributions would require multivariate approaches beyond the study’s scope.

The practical implications reach from the seed bag to the planting calendar. In the southern and eastern regions, where the frost-free period is longer, delayed sowing could shift flowering toward windows of lower atmospheric demand, though any adjustment must be coordinated with hybrid maturity selection to avoid truncating grain filling or raising late-season frost risk. In the northwest and west-central regions, where the frost-free window is tighter, choosing hybrids with enhanced drought and heat tolerance during pollination and grain filling becomes the primary management lever, tempered by the recognition that stress-tolerant hybrids may carry yield penalties in favorable years. The framework itself, built entirely from publicly available yield records and gridded climate data, is potentially transferable to the wider Corn Belt and other maize-producing regions, offering a scalable empirical route to matching agronomic strategy and breeding priorities to the environments where they will actually matter. As atmospheric drying intensifies with rising temperatures, knowing precisely where and when vapor pressure deficit strikes hardest may prove one of the most valuable tools Indiana’s farmers can hold.

Subject of Research: Environmental characterization of rainfed maize yield variability in Indiana using yield-based regionalization and climatic analysis

Article Title: Environmental characterization of maize yield variability in a major Corn Belt state

Article References: Environmental characterization of maize yield variability in a major Corn Belt state. (n.d.). https://doi.org/10.1007/s00704-026-06582-4

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06582-4

Keywords: maize, Indiana, Corn Belt, vapor pressure deficit, heat stress, yield variability, environmental regionalization, fuzzy c-means clustering, rainfed agriculture, climate variability, flowering stage, agricultural climatology