Genomes to fields 2024 maize genotype by environment prediction competition. [PDF]
Chen Q +34 more
europepmc +1 more source
Does an increase in plant diversity enhance agroecosystem services? Case study in rainfed rice based cropping systems in Madagascar [PDF]
Autfray, Patrice +6 more
core
Root trait dynamics of historical canola varieties under low and high nitrogen supply
Abstract Nitrogen fertilizer is a major input cost in crop production, making it crucial to enhance nitrogen use efficiency (NUE) to reduce fertilizer dependence without compromising yields. Root system architecture (RSA) is a key determinant of nutrient acquisition, particularly under nutrient‐limited conditions.
Shankar Pahari, Raju Soolanayakanahally
wiley +1 more source
Health assessment of wheat agroecosystems in Iran. [PDF]
Niazmoradi M +4 more
europepmc +1 more source
Spatial and temporal scales in plant phenotyping for crop water stress assessment: A review
Abstract Water stress is a major limiting factor for crop productivity worldwide, and its impacts are intensifying due to climate variability and increasing water scarcity. This review focuses on the spatial and temporal scales in plant phenotyping as a critical approach to improving crop water‐stress assessment and supporting precision water ...
Daniel Kingsley Cudjoe +3 more
wiley +1 more source
Crop performance and profitability for the initial transition years of a regenerative cropping system in the Upper Midwest United States. [PDF]
Datta A +7 more
europepmc +1 more source
Abstract Agricultural systems are vulnerable to extreme weather, market volatility, and changing socio‐cultural contexts. Despite efforts to create transformational solutions in agriculture to ensure economic, social, and environmental sustainability, there is often a disconnect between research findings and real‐world experience.
Alison J. Duff +2 more
wiley +1 more source
Are historical trends in weather consistent with model predictions in the Central United States? [PDF]
Baffaut C +8 more
europepmc +1 more source
Machine learning‐based prediction of cereal rye cover crop biomass across diverse agroecosystems
Abstract Accurate operational predictions of cereal rye (Secale cereale L.) biomass are critical for quantifying the agroecosystem services provided by cover crops and for guiding growers’ management decisions for subsequent cash crops. In this study, we developed machine learning‐based biomass prediction models using two advanced gradient‐boosted tree
Utsab Ghimire +6 more
wiley +1 more source
From pesticides use to agroecology: Survey dataset on farming systems in the Lake Guiers area in Senegal. [PDF]
Traoré MB +11 more
europepmc +1 more source

