Results 161 to 170 of about 116,042 (285)

Bacillus aryabhattai and silicon modulate soil biological activity and performance of Mimosa caesalpiniifolia under water deficit. [PDF]

Braz J Microbiol
da Silva Mota K, Garcia KGV, de Sousa Almeida M, Barbosa FLA, Borges WL, Mesquita RO, Araujo ASF, de Sousa GG, de Araujo Pereira AP.   +8 more
europepmc   +1 more source

Ineffectiveness of foliar nutrient sprays on cotton lint yield after riverine flooding

Agrosystems, Geosciences &Environment, Volume 9, Issue 1, March 2026.
Abstract Irrigated Australian cotton (Gossypium hirsutum L.) is primarily produced on Vertosols, which are susceptible to waterlogging due to their highwater retention capacity and poor drainage. Waterlogging inhibits root growth and depletes soil oxygen, resulting in changes to soil pH, redox, and nutrient availability. Australian cotton farmers often
Blake Palmer, Gunasekhar Nachimuthu, Stacey Cunningham, Andy Hundt, Graeme Schwenke   +4 more
wiley   +1 more source

Extreme Rainfall Contribution and Isotopic Excursion of Tropical Storm Alberto in Northeastern Mexico. [PDF]

Rapid Commun Mass Spectrom
Ibarra-Alejos OJ, Sánchez-Murillo R, Ventura-Houle R, Mansilla OG, Peña-Alonso T, Macías-Hernández BA, González-Hita L, Mejía-González MA.   +7 more
europepmc   +1 more source

Simulating climate change impacts on lentil yield in the Canadian Prairies using the CSM‐CROPGRO‐lentil model

Agronomy Journal, Volume 118, Issue 2, March/April 2026.
Abstract Lentil (Lens culinaris Medik.) has been widely adopted in crop rotations in the semiarid Canadian Prairies. Lentil is a known drought‐tolerant crop, but it is uncertain how a changing climate might impact lentil yields. Using a newly adapted lentil model, we simulated lentil yields in the Canadian Prairies with future climate scenarios based ...
Budong Qian, Qi Jing, Kui Liu, Ward Smith, Guillaume Jégo, Brian Grant, Marianne Crépeau, Yinsuo Zhang, Kenneth J. Boote, Gerrit Hoogenboom   +9 more
wiley   +1 more source

Indigenous Comprehension of Fodder Trees and Shrubs in Semiarid Areas of Metema District, Northwestern Ethiopia. [PDF]

ScientificWorldJournal
Melkamu Y, Masresha G, Ayenew T, Alemu A, Tadesse D.   +4 more
europepmc   +1 more source

Development of eNA Sampling for Early Detection of Pathogens in On‐Farm Water Sources

Environmental DNA, Volume 8, Issue 2, March/April 2026.
We compare four water‐sampling methods for detecting microbial and viral eNA in livestock troughs and show all approaches effectively recover community profiles, supporting flexible, field‐ready surveillance of livestock pathogens. ABSTRACT Early detection of livestock pathogens is critical for mitigating risk and implementing timely control or ...
Maxine P. Piggott, Allyson Malpartida
wiley   +1 more source

Aquatic Macrophytes in the Remediation of Atrazine in Water: A Study on Herbicide Tolerance and Degradation Using Eichhornia crassipes, Pistia stratiotes, and Salvinia minima

ACS Omega
María Carolina Ramírez Hernandez, Jesley Nogueira Bandeira, Deisy Alexandra Rosero Alpala, Lucrecia Pacheco Batista, Mayara Alana Silvestre Araújo, Paulo Sergio Fernandes das Chagas, Daniel Valadao Silva, Elis Regina Costa de Morais   +7 more
doaj   +1 more source

Natural and artificial abiotic factors: impacts on the seminal quality of Dorper rams in a semiarid region. [PDF]

Int J Biometeorol
Marinho GTB, Pandorfi H, da Silva MV, da Silva WA, Desenzi R, Batista AM, de Almeida GLP, Guiselini C, Moraes AS.   +8 more
europepmc   +1 more source

Alternative Insurance Indexes for Drought Risk in Developing Countries [PDF]

The paper compares the risk coping potential of insurances that are based on indices derived from weather (rainfall and temperature) data as well as from crop model and remote sensing analyses.
Bobojonov, Ihtiyor, Sommer, Rolf
core   +1 more source

Predicting Parameters Affecting Building Energy Consumption Using Machine Learning Models

Energy Science &Engineering, Volume 14, Issue 3, Page 1474-1491, March 2026.
This framework integrates long‐term weather forecasting with building energy simulation to predict energy demand. The CNN–LSTM model achieved the highest accuracy (R2 ≈ 0.95), supporting smart HVAC control with ~ 15% energy savings. ABSTRACT The rapid growth of population and the construction industry have led to an increase in energy demand and an ...
Bassam Musheer Kareem, Abdolsalam Ebrahimpour, Jafar Ghafouri, Roghayyeh Motallebzadeh   +3 more
wiley   +1 more source