Results 121 to 130 of about 2,199 (200)

Crop Growth Models: Development, Applications, Recent Advances, and Future Perspectives. [PDF]

Plants (Basel)
Li G, Wang Y, Zhao Y, Liu Z, Li S, Huang X.   +5 more
europepmc   +1 more source

Spatiotemporal moisture digital reconstruction of root zone and precision irrigation using FDR-HY2D for facility-based strawberry. [PDF]

NPJ Sci Food
Tang R, Luen LC, Tang J, Aridas NK, Talip MSA.   +4 more
europepmc   +1 more source

Comparative analysis of monocropping and mixed cropping systems on selected soil properties, soil organic carbon stocks, and simulated maize yields in drought-hotspot regions of Rwanda. [PDF]

Heliyon, 2023
Hashakimana L, Tessema T, Niyitanga F, Cyamweshi AR, Mukuralinda A.   +4 more
europepmc   +1 more source

Comparison of the machine learning and aquacrop models for quinoa crops

One of the main causes of low crop efficiency in Peru is poor management of water resources; that is why the main objective of this article is to estimate the amount of irrigation water required in quinoa crops through a comparison between the machine learning and Aquacrop models. For the development of this study, meteorological data from the province
Chumbe Llimpe, Rossy Jackeline, Silva Paucar, Stefany Dennis   +1 more
openaire   +1 more source

Study of Response of Cotton Productivity in Southern Xinjiang to Planting Patterns and Water-Nitrogen Management. [PDF]

Plants (Basel)
Lv T, Bian M, Chen F, Chen C, Wang M.
europepmc   +1 more source

Estimating Maize Yield in the Black Soil Region of Northeast China Using Land Surface Data Assimilation: Integrating a Crop Model and Remote Sensing. [PDF]

Front Plant Sci, 2022
Cui Y, Liu S, Li X, Geng H, Xie Y, He Y.
europepmc   +1 more source

A new vector-based framework combining AquaCrop, CROPWAT, WEAP, and multi-objective programming for optimizing crop allocation under water scarcity. [PDF]

Sci Rep
Mousavi M, Karimi M, Pilehforooshha P, Ahmadlou M, Farhadi-Bansouleh B.   +4 more
europepmc   +1 more source

A Hybrid Approach to Tea Crop Yield Prediction Using Simulation Models and Machine Learning. [PDF]

Plants (Basel), 2022
Batool D, Shahbaz M, Shahzad Asif H, Shaukat K, Alam TM, Hameed IA, Ramzan Z, Waheed A, Aljuaid H, Luo S.   +9 more
europepmc   +1 more source

Modeling the impacts of climate-smart practices on soil-water interaction and wheat yield under climate change in central Ethiopia. [PDF]

Sci Rep
Biratu AA, Bedadi B, Gebrehiwot SG, Ademe F, Hordofa T, Asmamaw DK, Abera W, Adimassu Z, Demessie SF, Tamene L, Melesse AM.   +10 more
europepmc   +1 more source

Optimizing irrigation scheduling for winter wheat using the AquaCrop model in Xinjiang, China. [PDF]

Front Plant Sci
Fu Q, Liu X, Hong M, Ai P, Ma L, Zhao J, Wang Q.   +6 more
europepmc   +1 more source