The Precision Nitrogen Project (PNP) worked with more than 80 corn and winter wheat
producers to inexpensively design and implement randomized, replicated field strip trials on …

This research introduces the PEnsemble 4 model, a weighted ensemble prediction model
that integrates multiple individual machine learning models to achieve accurate maize yield …

Abstract Precision Agriculture Technologies (PATs) are providing a great potential in
alleviating adverse impacts arising from climate change. This study evaluates the decision …

Nitrogen is one of the most important macronutrients for crops, and, in conjunction with
artificial intelligence algorithms, it is possible to estimate it with the aid of vegetation indices …

Inadequate nitrogen (N)-fertilisation practices, that fail to consider seasonally variable
weather conditions and their impacts on crop yield potential and N-requirements, cause …

In socioeconomic factor studies, the use of the logit and probit models has the disadvantage
of representing random variation in preference for unobserved components and time-linked …

Geographically weighted regression (GWR) has been presented as a valuable tool for
estimating site‐specific yield response functions to derive recommendations of variable rate …

Nitrogen is one of the most important macronutrients for crops, and in conjunction with
artificial intelligence algorithms, it is possible to estimate it with the aid of vegetation indices …

This study highlights the feasibility of using SAR data as a surrogate for optical acquisitions
in the generation of nitrogen prescription maps in wheat cultivation. Unlike the optical-based …

Big data and machine learning have the potential to transform agriculture and 4R nutrient
management practices. The integration of these technologies empowers farmers to adapt to …