The potential for operations research (OR) with farmer-supplied data coupled with machine
learning (ML) to improve crop management is explored through a series of case studies from …

Increasing global CO2 emissions have profound consequences for plant biology, not least
because of direct influences on carbon gain. However, much remains uncertain regarding …

Knowledge is a critical enabling factor for healthy agri-food innovation systems (AIS). AIS
and related knowledge management (KM) frameworks face significant implementation …

Global gridded crop models (GGCMs) are essential tools for estimating agricultural crop
yields and externalities at large scales, typically at coarse spatial resolutions. Higher …

Farmers can manage their crops and farms better if they can communicate their
experiences, both positive and negative, with each other and with experts. Digital agriculture …

Abstract Soil Organic Carbon (SOC) content is a key element for soil fertility and productivity,
nutrient availability and potentially represents a measurement of the sink for greenhouse …

Understanding the historical and future spatio-temporal changes in climate extremes and
their potential risk to rice production is crucial for achieving food security in Bangladesh. This …

The development of crop varieties with stable performance in future environmental
conditions represents a critical challenge in the context of climate change. Environmental …

The rapid expansion of soybean-growing areas across Europe raises questions about the
suitability of agroclimatic conditions for soybean production. Here, using data-driven …

Dataset quality heavily impacts the predictive performance of data-driven modelling. This
issue can be exacerbated in the prediction of agricultural production due to the complex …