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Un trabajo liderado por el investigador del Instituto de Agricultura Sostenible del Consejo Superior de Investigaciones Científicas (IAS-CSIC) Alberto...

Management zones are an important aspect of precision agriculture as they help to define spatiotemporal areas that share homogenous attributes for sit…

El IAS-CSIC adapta el índice CWSI a encinares mediterráneos para mapear el estrés hídrico y anticipar el decaimiento en dehesas frente al clima.

Un estudio liderado por el IAS-CSIC adapta el índice CWSI al seguimiento del estrés hídrico en encinares mediterráneos

Un trabajo liderado por el investigador del Instituto de Agricultura Sostenible del Consejo Superior de Investigaciones Científicas (IAS-CSIC) Alberto Hornero valida el uso del índice CWSI en dehesa y...

Un trabajo liderado por el investigador del Instituto de Agricultura Sostenible del Consejo Superior de Investigaciones Científicas (IAS-CSIC) Alberto Hornero valida el uso del índice CWSI en dehesa y...

Un estudio ha demostrado que el índice CWSI (Crop Water Stress Index), utilizado habitualmente en agricultura, puede adaptarse a los encinares mediterráneos para evaluar su estrés hídrico a partir de ...

NOTICIA: Un trabajo liderado por Alberto Hornero, investigador del Instituto de Agricultura Sostenible del Consejo Superior de Investigaciones Científicas (IAS-CSIC), ha demostrado que el índice CWSI ...

UAV hyperspectral/thermal image analysis for vineyards: compute vegetation indices (NDVI, PRI, MCARI, CWSI), develop spectral signature methods, validate wit...

In this part we explore the farm!Banjo has opened up access to the first world, so now it's time to explore it and see if we can find Kazooie...Next Part: (L...

Water security in Peninsular Malaysia remains a critical policy challenge due to fragmented governance, urban expansion, and the uneven distribution of freshwater resources. This study aims to address these challenges by establishing a state-level consolidated water security index (CWSI) that integrates five dimensions, eleven indicators and twenty-nine variables. The CWSI is used to (i) evaluate the current status of water security in eleven states of Peninsular Malaysia and (ii) identify the key risks and challenges associated with water security. Weights for each dimension, indicator, and variable were calculated using the AHP method to ensure methodological rigour. Additionally, normalisation and sensitivity analysis were conducted to ensure cross-state comparability and index robustness. Spatial disparities are clearly evident in the findings. States, such as Terengganu (CWSI = 0.612) and Pahang (CWSI = 0.608), demonstrate strong performance. Meanwhile, Perlis (CWSI = 0.408) and Selangor (CWSI = 0.414) remain vulnerable to persistent water security issues due to dam storage limitations, non-revenue water losses and insufficient treatment capacity. This study suggests that the CWSI be integrated into state-level governance frameworks to guide infrastructure investment, enhance accountability and align performance monitoring with national policies, such as the Water Sector Transformation 2040 (AIR 2040) and the Sustainable Development...

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Un trabajo liderado por el investigador del Instituto de Agricultura Sostenible del Consejo Superior de Investigaciones Científicas (IAS-CSIC) Alberto Hornero valida el uso del índice CWSI en dehesa y demuestra su potencial para comparar el estrés hídrico entre años, zonas y condiciones meteorológicas distintas, manteniendo su relevancia para la detección temprana de procesos de decaimiento como la Seca.

The temperature-based crop water stress index (CWSI) is the most robust metric among precise techniques that assess the severity of crop water stress, particularly in susceptible crops like maize. This study used a unmanned aerial vehicle (UAV) to remotely collect data, to use in combination with the random forest regression algorithm to detect the maize CWSI in smallholder croplands. This study sought to predict a foliar temperature-derived maize CWSI as a proxy for crop water stress using UAV-acquired spectral variables together with random forest regression throughout the vegetative and reproductive growth stages. The CWSI was derived after computing the non-water-stress baseline (NWSB) and non-transpiration baseline (NTB) using the field-measured canopy temperature, air temperature, and humidity data during the vegetative growth stages (V5, V10, and V14) and the reproductive growth stage (R1 stage). The results showed that the CWSI (CWSI < 0.3) could be estimated to an R2 of 0.86, RMSE of 0.12, and MAE of 0.10 for the 5th vegetative stage; an R2 of 0.85, RMSE of 0.03, and MAE of 0.02 for the 10th vegetative stage; an R2 of 0.85, RMSE of 0.05, and MAE of 0.04 for the 14th vegetative stage; and an R2 of 0.82, RMSE of 0.09, and MAE of 0.08 for the 1st reproductive stage. The Red, RedEdge, NIR, and TIR UAV-bands and their associated indices (CCCI, MTCI, GNDVI, NDRE, Red, TIR) were the most influential variables across all the growth stages. The vegetative V10 stage exhibited the most optimal prediction accuracies (RMSE = 0.03, MAE = 0.02), with the Red band being the most influential predictor variable. Unmanned aerial vehicles are essential for collecting data on the small and fragmented croplands predominant in southern Africa. The procedure facilitates determining crop water stress at different phenological stages to develop timeous response interventions, acting as an early warning system for crops.

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Sister Francisca Ngozi Uti, the founder and executive director of the Centre for Women Studies and Intervention in Abuja, Nigeria, won he 2024 Opus Prize.