Enhancing chickpea irrigation efficiency, yield and sustainability

Non-invasive method to assess chickpea water status, providing farmers with an effective tool for optimizing irrigation schedules and potentially enhancing the sustainability of chickpea farming! This approach has the capacity to revolutionize chickpea management by not only increasing crop yields but also improving water efficiency. The implications extend beyond the farm, impacting global food security and contributing positively to environmental concerns.

New study published by the Hebrew University of Jerusalem (HUJI) introduces a non-invasive technique for evaluating chickpea water status, offering farmers a powerful tool to fine-tune irrigation schedules and potentially elevate the sustainability of chickpea cultivation. This method holds the potential to transform chickpea management, amplifying both crop yields and water efficiency. Its ramifications stretch far beyond the agricultural realm, resonating with global food security efforts and addressing pressing environmental challenges.

The remote sensing aspect of the project is led by researchers at the Hebrew University, including Dr. Ittai Herrmann from The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture. PhD. candidate Roy Sadeh (HUJI) trained and tested spectral models for quick and non-invasive assessment of chickpea water status based on leaf water potential estimation from space and ground. The agronomical aspects were covered by Hebrew University PhD. student Asaf Avneri under the guidance of Dr. Ran Lati (ARO) and Prof. Shahal Abbo (HUJI) and with Dr. David Bonfil (ARO). This innovative approach holds immense promise for transforming agriculture practices, particularly in regions facing water scarcity.

Chickpea, also known as garbanzo beans, is a crucial global grain legume, serving as a staple protein source around the world and especially in the Middle East, South Asia and the Mediterranean. The proposed method holds transformative potential for agriculture by enabling farmers to optimize irrigation schedules efficiently. This could lead to increased crop yields and improved water use efficiency, contributing to resource conservation and reduced environmental impact. Furthermore, the innovation has broader implications for global food security, showcasing the impact of advanced precision-smart agricultural technologies on sustainable farming practices.

The study, conducted in two farm experiments and two commercial fields, used ground-based hyperspectral imaging and satellite images from the Vegetation and Environment monitoring on New Micro-Satellite (VENmS) program. It aimed to remotely measure leaf water potential of field-grown chickpeas under different irrigation treatments. While doing so, the limited effect of leaf area index on the ability to remotely estimate leaf water potential was revealed.

Roy Sadeh developed spectral estimation models using vegetation indices and machine learning based on all spectral bands. The study demonstrated that the normalized difference spectral index (1600 and 1730 nm) provided the most accurate estimation of leaf water potential amongst the vegetation indices. While the artificial neural network models improved the assessment accuracy and performed similarly well for ground and spaceborne data. The new method offers significant benefits to farmers by providing a rapid, non-destructive tool to enhance irrigation scheduling in chickpea fields, potentially improving variable rate irrigation management. Additionally, this tool holds promise for physiologists and breeders in screening for drought-tolerant chickpea genotypes, paving the way for sustainable farming practices on a larger scale. The next step of the project is combining space-borne spectral data to improve leaf water potential estimation is ongoing and Omer Perach (a PhD candidate) has presented very nice preliminary results (ECPA 2023) and the additional paper is being written these days.