Forecasting crop yields, or providing an expectation of ex-ante harvest amounts, is highly
relevant to the whole agricultural production chain. Farmers can adapt their management …

Crop yield forecasting at national level relies on predictors aggregated from smaller spatial
units to larger ones according to harvested crop areas. Such crop areas come from land …

Crop type mapping visualizes the spatial distribution patterns and proportions of the
cultivated areas with different crop types, and is the basis for subsequent agricultural …

The use of ML in agronomy has been increasing exponentially since the start of the century,
including data-driven predictions of crop yields from farm-level information on soil, climate …

Remotely sensed Earth observations (EO) have their history firmly rooted in agricultural
monitoring, and more recently with applications in food production, food security, and …

Seasonal climate forecasts are a key component of sectoral climate services. Skill and
reliability in predicting agro-climate indicators, co-designed with and for European wheat …

Timely knowledge of phenological development and crop growth is pivotal for evidence-
based decision making in agriculture. We propose a near real-time approach combining …

Over the last two decades or so, there has been many research carried out to understand
the mechanics and spatial distribution of soil loss by water erosion and to a lesser extent of …

A wide variety of methods exist nowadays to address the important problem of estimating
crop yields from available remote sensing and climate data. Among the different …

Context Against a background of unprecedented climate change, humanity faces the
challenge of how to increase global food production without compromising the natural …