The recent jump in grain prices is another reminder of the importance of having high cropland productivity. Although the Ukraine war has pushed markets over the edge, the underlying conditions were already strained, in part due to weather-driven shortfalls around the world. As scientists try to identify ways to boost productivity in the face of ongoing climate change, all options should be on the table, even those we haven’t historically paid much attention to.
Combining experiments and satellite observations to measure yield benefits from crop rotation
Population growth and the unprecedented rate of climate change create a need for rapid agricultural adaptation. Therefore, it is important to answer questions about which agricultural practices are most effective for growing crops under various weather conditions. There are two main ways that researchers try to answer such questions, but as we will discuss below, neither approach is perfect.
Space lasers to the rescue: using lidar to map crop types
In many regions we lack geo-located ground data on where different crop types are located. This has limited the ability to map crop types with satellite data, because methods typically need a lot of local ground training data to work well. How would Global Ecosystem Dynamics Investigation (GEDI) help?
A new method to improve crop mapping: how to give two shifts
While it may seem trivial to determine which crops are growing in each field, crop type maps have historically been constructed using expensive and time consuming on-the-ground field surveys. In many parts of the world, such field surveys are not conducted due to cost. To address this, there have been many recent efforts to construct crop type maps using a combination of cheap and abundant satellite imagery and a small number of crop type labels from the expensive field surveys. In our recent paper, we develop a method that can be used to construct crop type maps in settings where labelled data is constrained to one geographical region and the goal is to construct a crop type map in nearby regions.
The impacts of climate change are all around us. Wildfire seasons are increasingly severe, hurricane seasons are longer, and coastal flooding is more frequent. A more subtle, but perhaps more pernicious, impact has been the steady and inexorable march upwards in global average temperatures.
In a recent study, we focus on one consequence of increased temperatures that is relevant for anyone with children or who cares about how we educate future generations. We examine how exposure to higher temperatures during the school year impacts how much students learn when they are in school.
It’s now become a regular topic in our newsfeeds: wildfires are increasing in number, size, and damage every year. Some of the consequences of these fires are obvious but others are less well understood. Our new PNAS paper focuses on the increasingly important contribution of wildfire smoke to overall pollution levels in the US.
Digging deep from above: satellite-guided investigation of soil fertility and crop yield response
Low quality soils are the source of multiple problems in global smallholder agriculture. Additionally, degraded soils often limit the effectiveness of fertilizers, as the applied nutrients become unavailable to the crop due to adverse conditions in the soil. This cycle of soil degradation and low fertilization drives low food production and poverty in developing agricultural systems across the world. In a recent paper, we show how satellite data can help to break this cycle, by allowing one to rapidly assess which nutrients are most limiting in a region.
Satellite yield estimation: how good is it, and what can we learn?
With today’s computing power and free satellite imagery, generating a crop yield map from satellite data is relatively easy. Knowing if that map is accurate or not, however, can be surprisingly difficult. Just how good are satellite yield maps? Are we justified in using them to assess agricultural trends or yield impacts of management practices at field and sub-field scales?
Soybean is the United States’ most lucrative agricultural export, driven by increasing demand for animal feeds associated with rising meat consumption around the world. With such an important agricultural commodity, understanding spatial and temporal patterns in yield is of widespread interest. Where ground-truth data on yields are not available, leveraging remote sensing and machine learning to accurately estimate farm yields can help provide these insights.
Is drought becoming more or less important for agriculture?
As the world continues to warm, many agricultural regions are seeing an increased frequency and severity of conditions that lead to drought. We know from decades of work that the ongoing climate trends are overwhelmingly negative for crops in many systems. In a recent study published in Nature Food, we look at the last two decades in the United States, and whether there is any evidence that new technologies have helped adapt to climate trends.
Mapping Crops with Smartphone Crowdsourcing, Satellite Imagery, and Deep Learning
Smallholder farms — holdings of less than 2 hectares — produce one-third of global food consumed, employ 2 billion people, and make up 84% of the world’s farms. Yet our collective knowledge of their food production remains limited: questions like what crop types smallholders are growing and where they grow them remain unanswered, making it hard to track yield progress, study farming practices, and design agricultural policies. The data gap exists because most smallholders are located in countries where the ability to conduct surveys — the traditional way of obtaining farm-level information — is still nascent or under development.