Results 131 to 140 of about 2,367 (204)

Irrigation and AquaCrop application usability in Finland

Ilmaston muuttuessa maatalouden toimintaympäristö muuttuu maailmanlaajuisesti. Maailmalla kasteluviljelyllä on tärkeä rooli, mutta Suomessa ei ole laajaa viljojen kasteluviljelyn perinnettä.
Iivonen, Katja
core  

Projected Changes in Yield and Water Use Efficiency of Cold-Region Rice and the Role of CO₂ Under Climate Change. [PDF]

Plants (Basel)
Li Z, Liu Y, Nie T, Xiao X, Guo H, Wang T, Han Y.   +6 more
europepmc   +1 more source

Irrigation modulates the effect of increasing temperatures under climate change on cotton production of drip irrigation under plastic film mulching in southern Xinjiang. [PDF]

Front Plant Sci, 2022
Wang H, Yin Z, Zhang L, Zhao F, Huang W, Wang X, Gao Y.   +6 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

Evaluation of aquacrop model to predict crop water productivity for paddy

, 2018
Water and nutrient are critical inputs for crop production, especially in meeting challenges from increasing fertilizer cost and irregular water availability associated with climate change. The Land and Water Division of Food and Agriculture Organization
Part Time Noor Husna binti Mohamad Zayadi
core  

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

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