Recently published research from Michigan State University shows that incorporating in-season water deficit information into remote sensing-based crop models significantly improves corn yield predictions.

The findings were published in Environmental remote sensinga leading journal in the field.

The project was led by Bruno Basso, an MSU Foundation professor in the departments of Earth and Environmental Sciences, and Plant, Soil, and Microbial Sciences, and at the WK Kellogg Biological Station. Alongside Basso was his graduate student Guanyuan Shuai.

Yield forecasts are of great importance, from national and international food supply chains to the individual producer. In addition to ensuring food security, very important financial decisions are made based on this information. Growers must decide how much fertilizer and other inputs to apply to their fields, for example, an area where costs have skyrocketed for many reasons, including climate change and global conflict.

“Accurate knowledge of yield forecasts before the end of the season is of paramount importance for grain prices, which affects the profitability of farmers, as well as commodity traders and food companies,” Basso said. .

Bruno Basso

Historical weather forecasts and crop yields from a given location are often used to predict next season’s performance, but this methodology has flaws. This can be unsettling when producers seek as much certainty as possible.

“Farmers can now receive high-resolution weather information on their tablets and smartphones, but the U.S. Department of Agriculture’s yield outlook is at the county level, so farmers can’t really use it to forecast yields. of their fields,” Basso said. “What farmers need is knowledge to better manage and predict yields at the field and sub-field level. We know that circumstances vary within a field, even a small one of a few acres, let alone operations that include thousands of acres.

“It’s important that farmers have confidence in the data they use to make decisions, and we’re trying to help them improve that decision-making process at the right scale. Farmers are interested in profitability, which is also linked to environmental sustainability.

For the project, Basso and Shuai assessed 352 fields of varying sizes in Michigan, Indiana, Illinois and Iowa. The team collected climate and soil data, in addition to more than 2,500 yield maps over several years – 2006 to 2019 – directly from farmers.

They obtained high-resolution images from the private company Planet, the European Space Agency and NASA, as well as digital elevation models from the US Geological Survey dataset.

The images were used in part to calculate the Green Chlorophyll Vegetation Index, an indication of plant vigor that measures leaf chlorophyll content based on infrared and near-infrared imagery.

Basso and Shuai then implemented the Systems Approach to Sustainable Land Use (SALUS) program, which models crop, nutrient, soil and water conditions every day for many years. using different management techniques. SALUS provided the Daily Crop Drought Index (CDI), designed to highlight the effect of in-season water shortages on crops.

“We found that the inclusion of CDI significantly improved in-season forecast accuracy,” Basso said. “We showed that the greatest forecast improvements were observed in the driest year, 2012. We also showed that spatial variations of maize yield subfields are better captured with the inclusion of the CDI for most fields.”

Basso believes that subdomain-level analyzes are a promising way to ensure accurate and precise predictions, and that decisions should be made based on real-time data rather than historical guidelines alone. He said that while some technologies are widely adopted, the rate at which this happens needs to be accelerated.

“Even if you use remote sensing imagery over your fields, that alone is not enough,” Basso said. “Once the canopy of the field approaches, which happens about a month after planting, remote sensing will only see the top layer of those leaves, as one big green layer. Two months later, he will see the same large green layer but will not be able to grasp that the leaves and the plant have grown significantly below the canopy.

“Our novel integrated approach of coupling crop-modeled water stress with high-resolution imagery attempts to remove this limitation in the analysis of remote sensing imagery. This approach provides more reliable and timely information for farmers’ cost savings and environmental protection. »


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