While the world population is steadily growing, the demand for plant-based protein in general, and chickpea in particular, is rising. Heatwaves and terminal droughts are the main environmental constraints on chickpea production worldwide. Thus, developing better irrigation management for the chickpea agro-system can promote higher and more sustainable yields. Strategic supplemental irrigation can dramatically increase yield when applied at the right time and in appropriate quantities. Here, we studied the response of a modern Kabuli chickpea cultivar to supplemental irrigation during the critical pod-filling period over three growing seasons (2019–2021) in northern Negev, Israel, under semi-arid conditions. Six irrigation treatments were applied based on irrigation factors of 0, 0.5, 0.7, 1.0, 1.2, and 1.4 of evapotranspiration (ET0), as measured by an on-site meteorological station. Morpho-physiological parameters and above-ground biomass accumulation were monitored throughout the cropping seasons, and the final grain yield was determined at maturation. Irrigation initiation was guided by the plants' leaf water potential (ΨLWP > 15 bar) in the field. Our results indicate that the optimal water status (as reflected by pressure chamber values) was 12–14 bar during the irrigation period. Furthermore, adhering to an irrigation strategy based on evapotranspiration with an irrigation factor of 1.2 resulted in the highest grain yields over the three-year study period. To ensure an optimal water supply during the reproductive phase compatible with the crop water requirements, maintaining a 25 mm node length above the last fully developed pod and a 90 mm distance between the last fully developed pod to the stem apex is recommended. In conclusion, initiating irrigation when the crop is already at mild drought stress, followed by sufficient irrigation while following the indicated morphology and water potential values, may help farmers optimize irrigation and maximize chickpea crop production.

Chickpea (Cicer arietinum L.) is a major grain legume grown worldwide as a staple protein source. Traditionally, it is a rain-fed crop, but supplemental irrigation can increase yields and counteract the challenges posed by the changing climate worldwide. A fast and non-destructive plant water status assessment method may streamline irrigation management. The main objective of this study was to remotely assess the leaf water potential (LWP) and leaf area index (LAI) of field-grown chickpea. Five irrigation treatments were applied in two farm experiments and two commercial fields. Ground hyperspectral canopy reflectance and Vegetation and Environment monitoring on a New Micro-Satellite (VENµS) images acquired throughout the study. In parallel, LWP and LAI measurements were captured in the field. Vegetation indices (VIs) and machine learning (ML) based on all spectral bands were used to calibrate and validate spectral estimation models. The normalized difference spectral index (NDSI) that used bands on 1600 and 1730 nm (NDSI(1600,1730)) selected in the current study yielded the LWP lowest estimation error on independent validation (RMSE = 0.19 [MPa]) using linear regression. VENµS based VIs resulted in relatively lower LWP estimation accuracy (RMSE = 0.23–0.29 [MPa]) compared to VIs calculated from ground hyperspectral data (RMSE = 0.19–0.21 [MPa]). Artificial neural network (ANN) models for LWP from ground and space spectral data showed similar performances (RMSE = 0.15–0.17 [MPa]), and were both more accurate than VIs. LWP response to the irrigation treatments was faster than the LAI response and was captured by the NDSI(1600,1730). The low correlation found between LWP and LAI (r = 0.08–0.44) supports the conclusion that spectral reflectance of chickpea canopy can be used to estimate LWP per se and is only partially affected by morphological changes induced by irrigation treatments and canopy development. The ability to rapidly estimate chickpea LWP may improve irrigation scheduling in the future.

Underutilized species (also known as orphan crops) present opportunities to increase crop diversity and food security. Such crops lack modern genetic tools and knowledge to facilitate efficient modern breeding approaches. A wide collection of tef (Eragrostis tef (Zucc.) Trotter) genotypes was used to identify genomic regions associated with productivity, lodging, and morpho-physiological traits under contrasting water availabilities. The obtained results are expected to enhance modern breeding and improve tef productivity under traditional and modern cropping systems, thus improving farmers' livelihood and food security.

Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as “Digital Twin”. This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring.