Water Stress Index Research Articles

Applicability of the crop water stress index based on canopy–air temperature differences for monitoring water status in a cork oak plantation, northern China

The crop water stress index based on canopy–air temperature differences (CWSIdT) is an appropriate index for monitoring the water status of croplands but is rarely used to evaluate the water status of forest ecosystems. In this study, we selected a cork oak plantation in the lithoid mountain area of northern China as the research object and observed the canopy temperature (Tc), actual evapotranspiration (ETa), and meteorological factors of the plantation continuously and synchronously during the period of canopy closure (May–September) in 2020 and 2021. Taking the crop water stress index based on the ratio of ETa to potential evapotranspiration (CWSIet) as a standard, the applicability of CWSIdT for detecting water status in the plantation was evaluated. The results showed that, under well-watered conditions, the canopy–air temperature differences (dT) and the canopy surface transpiration rate were both controlled by the net radiation received by the canopy surface (Rnc), and the warming effect of Rnc masked the cooling effect of latent heat on dT, resulting in a failure establishment of the non-water-stressed baseline in the plantation. Therefore, the empirical CWSIdT (CWSIdTe) is not suitable for evaluating the water status in this plantation. However, the theoretical CWSIdT (CWSIdTt), based on the energy balance and Penman–Monteith equation, captured daily variations in the water status in our plantation and showed a significant linear correlation with the measured relative available water content in the root zone (R2 = 0.59, P < 0.01) and CWSIet (R2 = 0.75, P < 0.01). The CWSIdTt showed an upward trend during the day, and midday (13:00−14:00) was the optimal time to measure dT for detecting the daily water status of the plantation. The CWSIdTt provides an effective, sensitive, and non-contact method of assessing daily water status changes in the cork oak plantation.

A data-driven high spatial resolution model of biomass accumulation and crop yield: Application to a fragmented desert-oasis agroecosystem

Information on crop yield is important for food security, in particular under the conditions of climate change and growing population worldwide. We developed a new fully distributed, high spatial resolution, model of biomass accumulation and crop yield applicable to a highly heterogeneous desert-oasis agroecosystem. The bulk of required input data is obtained by retrieving pixel-wise biogeophysical variables from a suite of very diverse satellite data. Both temperature and water stress conditions at field-scale are given full consideration, while the model was designed to strike a balance between model applicability and satisfactory characterization of the heterogeneous desert-oasis system to estimate field-scale yield. The development of this model relies on three main innovations. First, the start and end of the growing season were estimated for each pixel by calibrating the high spatial and temporal resolution observations of Normalized Difference Vegetation Index (NDVI) by Sentinal-2 (S2) MSI (Multi-Spectral Instrument) against limited local phenological information. Second, to monitor crop water stress, account taken of irrigation, a process-based water and energy balance model was applied to estimate the actual evapotranspiration (ET). This requires knowledge of soil water availability, which is characterized by downscaling the ASCAT (Advanced SCATterrometer) soil moisture data product. To capture the dominant features of the eco-hydrological conditions in the desert and oasis agroecosystem, ET was further downscaled from the 1 km resolution. Third, likewise the water stress indicator, the air temperature stress indicator was mapped after characterizing the thermal contrast and heterogeneity of the desert-oasis system, by generating time series of air temperature at 1 km spatial resolution using the MODIS (Moderate Resolution Imaging Spectroradiometer) Land Surface Temperature (LST) data product. In the temporal dimension, gaps were mitigated by applying time series analysis techniques to reconstruct cloud-free time series of LST, NDVI, fAPAR and albedo. These innovations add up to a high resolution characterization of crop response to the geospatial variability of weather and climate forcing in the desert-oasis agroecosystem. The model was applied to the dominant crops, i.e., spring wheat, maize, sunflower, and melon, in the oases of the Shiyang River Basin (northwestern China) characterized by a rather fragmented land use. The high resolution of pixel-wise ecohydrological parameters, i.e., crop phenology, temperature stress and water stress factors successfully reflect differences of crops with different phenology and location in the oases. The relative errors for wheat and maize yields compared to the census data are less than 5% at district level. At the county level, the relative errors of wheat yields of Liangzhou, Minqin, Gulang, Jinchuan, and Yongchang equal to 0.87%, 24.2%, 9.7%, 12.5%, and 7.2%. For maize, the dominant crop, the error on estimated yields was less than 5%, except in Gulang. The relative error on estimated yield for sunflower was less than 10% compared to agricultural census data. The relative error on estimated melon yield was 16%. This performance highlights the applicability of the model to estimate field-scale yields in agroecosystems characterized by fragmented land use.

Using Aerial Thermal Imagery to Evaluate Water Status in Vitis vinifera cv. Loureiro.

The crop water stress index (CWSI) is a widely used analytical tool based on portable thermography. This method can be useful in replacing the traditional stem water potential method obtained with a Scholander chamber (PMS Model 600) because the latter is not feasible for large-scale studies due to the time involved and the fact that it is invasive and can cause damage to the plant. The present work had three objectives: (i) to understand if CWSI estimated using an aerial sensor can estimate the water status of the plant; (ii) to compare CWSI from aerial-thermographic and portable thermal cameras with stem water potential; (iii) to estimate the capacity of an unmanned aerial vehicle (UAV) to calculate and spatialize CWSI. Monitoring of CWSI (CWSIP) using a portable device was performed directly in the canopy, by measuring reference temperatures (Tdry, Twet, and canopy temperature (Tc)). Aerial CWSI calculation was performed using two models: (i) a simplified CWSI model (CWSIS), where the Tdry and Twet were estimated as the average of 1% of the extreme temperature, and (ii) an air temperature model (CWSITair) where air temperatures (Tair + 7 °C) were recorded as Tdry and in the Twet, considering the average of the lowest 33% of histogram values. In these two models, the Tc value corresponded to the temperature value in each pixel of the aerial thermal image. The results show that it was possible to estimate CWSI by calculating canopy temperatures and spatializing CWSI using aerial thermography. Of the two models, it was found that for CWSITair, CWSIS (R2 = 0.55) evaluated crop water stress better than stem water potential. The CWSIS had good correlation compared with the portable sensor (R2 = 0.58), and its application in field measurements is possible.

Estimation of Evapotranspiration and Soil Water Content at a Regional Scale Using Remote Sensing Data

The timely and accurate estimation of soil water content (SWC) and evapotranspiration (ET) is of great significance in drought estimation, irrigation management, and water resources comprehensive utilization. The unsupervised classification was used to identify the crops in the region. Based on MOD16A2 and the meteorological data, a SEBS model was used to estimate the ET in the Jiefangzha Irrigation Field from 2011 to 2015. Based on the crop water stress index (CWSI), the SWC in 2014 was retrieved and verified with the measured SWC on different underlying surfaces (sunflower, corn, wheat, and pepper). The results showed that: (1) The positional accuracy of maize, sunflower, wheat, and pepper are 0.81, 0.80, 0.90, and 0.82, respectively; (2) The annual ET from 2011 to 2015 presented well the spatial distribution of the ET within the field; (3) The validation results of the estimated SWC on the underlying surface of wheat and sunflower showed a good robustness, the R2 was 0.748 and 0.357, respectively, the RMSE was 2.61% and 2.309%, respectively, and the MAE was 2.249% and 1.975%, respectively. However, for maize and pepper with more irrigation times, the SWC estimation results, based on the CWSI were poor, indicating that the method was more sensitive to soil drought and suitable for the crop SWC estimation with less irrigation and drought tolerance. The results can provide a reference for the agricultural water resources management and the irrigation forecast at a regional scale.

Deep neural network based interactive fuzzy Bayesian search algorithm for low‐cost smart farming automation model

SummaryOne of the most significant factors that influence the globalized economy is agriculture. In order to address the requirement of increasing populations in terms of food necessities, modernizations and technological progressions in agriculture, it is necessary to implement a smart agricultural system. Various traditional techniques are still utilized by the farmers and their intuition in agriculture is not enough to furnish and deliver various issues namely soil management, plant disease identification, weed management, irrigation management, and so forth. Therefore, in this paper, a low‐cost smart farming automation system is evaluated and presented. Since the development of a smart farming automation system minimizes the labor cost and enhances agricultural production level, this paper proposes a deep neural network based Interactive fuzzy Bayesian search (DNN‐IFBS) algorithm for a low‐cost smart farming automation system. In addition to this, the crop water stress index (CWSI) is computed to determine the water status of the plant by employing solar irradiation, canopy temperature, and so forth. The data for analysis is collected simultaneously from three cameras containing image resolutions of about 650 × 460 pixels each. Two different plants namely the melon and sesame are utilized as datasets for experimentation. Finally, the performances of the proposed approach are determined according to the statistical performance measures namely accuracy, specificity as well as F‐measure. The comparative analysis is carried out to evaluate the effectiveness of the proposed system. From the evaluation results, the accuracy rate obtained for the proposed approach is 98.7%.

Analyses of the Impact of Soil Conditions and Soil Degradation on Vegetation Vitality and Crop Productivity Based on Airborne Hyperspectral VNIR–SWIR–TIR Data in a Semi-Arid Rainfed Agricultural Area (Camarena, Central Spain)

Soils are an essential factor contributing to the agricultural production of rainfed crops such as barley and triticale cereals. Changing environmental conditions and inadequate land management are endangering soil quality and productivity and, in turn, crop quality and productivity are affected. Advances in hyperspectral remote sensing are of great use for the spatial characterization and monitoring of the soil degradation status, as well as its impact on crop growth and agricultural productivity. In this study, hyperspectral airborne data covering the visible, near-infrared, short-wave infrared, and thermal infrared (VNIR–SWIR–TIR, 0.4–12 µm) were acquired in a Mediterranean agricultural area of central Spain and used to analyze the spatial differences in vegetation vitality and grain yield in relation to the soil degradation status. Specifically, leaf area index (LAI), crop water stress index (CWSI), and the biomass of the crop yield are derived from the remote sensing data and discussed regarding their spatial differences and relationship to a classification of erosion and accumulation stages (SEAS) based on previous remote sensing analyses during bare soil conditions. LAI and harvested crop biomass yield could be well estimated by PLS regression based on the hyperspectral and in situ reference data (R2 of 0.83, r of 0.91, and an RMSE of 0.2 m2 m−2 for LAI and an R2 of 0.85, r of 0.92, and an RMSE of 0.48 t ha−1 for grain yield). In addition, the soil erosion and accumulation stages (SEAS) were successfully predicted based on the canopy spectral signal of vegetated crop fields using a random forest machine learning approach. Overall accuracy was achieved above 71% by combining the VNIR–SWIR–TIR canopy reflectance and emissivity of the growing season with topographic information after reducing the redundancy in the spectral dataset. The results show that the estimated crop traits are spatially related to the soil’s degradation status, with shallow and highly eroded soils, as well as sandy accumulation zones being associated with areas of low LAI, crop yield, and high crop water stress. Overall, the results of this study illustrate the enormous potential of imaging spectroscopy for a combined analysis of the plant-soil system in the frame of land and soil degradation monitoring.

The growing water crisis in Central Asia and the driving forces behind it

Central Asia (CA) is one of the most severe water crisis areas on earth, which has seriously limited the achievement of sustainable development goals (SDGs) in the region. However, a multi-perspective analysis on the process and driving factors of the water crisis in CA has not been conducted. Therefore, we assess the water crisis from multiple perspectives using the water stress index (WSI), safe drinking water and water pollution indicators, and quantitatively analyze the impact of climate change, population growth, poverty, urbanization and transboundary river management on the water crisis. Results show that the water crisis in CA is intensifying. Uzbekistan and Turkmenistan belong to the “severe water stress” category, and the WSIs are increasing in both countries. Tajikistan is classified as “high water stress”. Kyrgyzstan and Kazakhstan both exhibit “moderate water stress”. Moreover, the proportion of the rural population with access to safe drinking water is significantly lower than that of the urban population in all the CA countries. The impact of human activities on water crisis in CA is more significant than that of climatic factors. Both cultivated land area and population are significant factors affecting the water crisis in CA (p < 0.05), with the regression coefficients of 0.62 and 1.62, respectively. Our research provides an essential reference for the sustainable management of water resources and warns that the water security situation in CA will worsen if no effective action is taken.

‘Asian water towers’ are not a sustainable solution to the downstream water crisis

Quantifying climate change- and socioeconomic development-induced changes in the ‘water tower’ function is a major challenge in high-mountain Asia (HMA), especially when considering the affected downstream areas. This study applies historical monsoon-influenced rainfall patterns and the water stress index to quantify the climate change- and socioeconomic growth-induced changes in the ‘Asian water tower’ function through the middle of the 21st century by nonparametric empirical quantile mapping and empirical orthogonal function analysis. Water pressures are predicted to intensify in 25.84 ± 7.87 % of ‘water tower’ downstream watersheds. Enhancements in the ‘Asian water tower’ function are vulnerable to help stop the deterioration of downstream water pressure while increasing the water sufficiency probability by 7.97 ± 15.52 %. Water withdrawals are projected to explain 55.90 ± 21.77 % of water security in the ‘water tower’ function-affected watersheds in 2050; thus, this study calls for more effective policies and the development of technologies to reduce the water crisis in downstream HMA.

Assessment of crop traits retrieved from airborne hyperspectral and thermal remote sensing imagery to predict wheat grain protein content

Wheat (Triticum spp.) is among the world’s most widely grown crops and receives large quantities of nitrogen (N) fertiliser. Grain protein content (GPC) is influenced by genetic, agronomic and weather conditions affecting crops’ physiological status and stress levels; accurate GPC prediction has potential to reduce N losses and improve profit. Success in GPC estimation from remotely sensed plant traits has been limited. For progress to be made, it is necessary to robustly identify imaging spectroscopy-based physiological traits most closely associated with GPC in both experimental and commercial contexts. We present results from piloted hyperspectral flights and ground campaigns at two dryland field experiments with divergent water supply and wide-ranging N treatments, and from two years’ flights over 17 commercial fields planted to either bread (T. aestivum) or durum (T. durum) wheat, in the southern Australian wheat belt. Imagery was acquired with airborne hyperspectral and thermal sensors, with spatial resolutions of approx. 0.3 m and 0.5 m for experimental plots and 1 m/1.7 m in commercial fields. Leaf clip measurements, leaf and grain samples were collected and, in commercial fields, ∼40,000 records obtained from harvester-mounted protein monitors. Crop Water Stress Index (CWSI), solar-induced fluorescence (SIF), reflectance indices and PRO4SAIL radiative transfer model inverted parameters were retrieved for each plot and GPC record location. The photochemical reflectance index (PRI) related to xanthophyll pigments was consistently associated with GPC at both leaf and canopy scale in the plots and transect. In the commercial crops, a gradient boosted machine learning algorithm (GBM) ranked CWSI as the strongest indicator of GPC under severe water stress, while SIF, PRI and inverted biochemical constituents anthocyanins and carotenoids were consistently important under more benign conditions. Structural parameters inverted from the hyperspectral reflectance imagery were not prominent except under severe drought. We attained statistically significant results estimating GPC in unseen samples, with best relationships between predicted and observed GPC of r2 = 0.80 in a model built with thermal and physiological traits obtained from the hyperspectral and thermal imagery.

Assessment of Water Stress Conditions in Central Italy by the Use of Ground and Remotely Sensed Weather Datasets

Aridity and drought, which are determined by climatic and temporary water scarcity, respectively, are important limiting factors for plant gross primary production. These phenomena are commonly assessed and/or monitored by means of weather indices, most of which are based on observations of precipitation and potential evapotranspiration. The estimation of such indices over large areas can be carried out using multiple datasets, i.e., those derived from weather stations, satellite images, and ground radars. The possibility of using interpolated or remotely sensed datasets in place of ground measurements was currently investigated for Tuscany, a region in Central Italy, showing complex and heterogeneous environmental features. The former weather datasets were first evaluated versus corresponding ground measurements. Next, the basic weather variables were combined and cumulated over 30–60 days to yield synthetic indicators of water deficit, which were assessed in the same way. Finally, these indicators were evaluated to predict the soil water conditions of a meadow and an olive grove during the 2021 summer period. The results obtained indicate that the use of the multi-source weather datasets induces only a minor deterioration of the water stress indicators and is therefore efficient to monitor the water status of different ecosystems with high spatial (200 m) and temporal (daily) details.

Rising agricultural water scarcity in China is driven by expansion of irrigated cropland in water scarce regions

Rising agricultural water scarcity in China is driven by expansion of irrigated cropland in water scarce regions

Real-time irrigation scheduling of maize using Degrees Above Non-Stressed (DANS) index in semi-arid environment

Irrigation scheduling methods have been used to determine the timing and amount of water applied to crops. Scheduling techniques can include measurement of soil water content, quantification of crop water use, and monitoring of crop physiological response to water stress. The aim of this study was to evaluate the performance of a simplified crop canopy temperature measurement (CTM) method as Irrigation Principles. Soil and Water Conservation Engineera technique to schedule irrigation for maize. Specifically, the Degrees Above Non-Stressed (DANS) index, which suggests water stress when canopy temperature exceeds the non-stressed canopy temperature (Tcns), was determined by estimating Tcns from a weather based multilinear regression model. The modeled Tcns had a strong correlation with observed Tcns with a pooled R2 values of 0.94 across the 2018, 2019, and 2020 growing seasons. This DANS index was also highly correlated with the conventionally used Crop Water Stress Index (CWSI) with R2 values of 0.67, 0.59, and 0.76 in 2018, 2019, and 2020, respectively. Furthermore, DANS had a strong linear relationship with soil water depletion above 60% in the 0.60 m soil profile with an R2 of 0.78. The CTM method was also compared to more commonly used scheduling methods namely: soil moisture monitoring (SMM) and crop evapotranspiration modeling (ETM). Grain yield was significantly lower for the CTM method than for the ETM method in 2018 and 2020 but not in 2019. No significant differences were observed in Irrigation Water Productivity (IWP) in 2018; however, all treatments were significantly different with the CTM method having the greatest IWP in 2020. For attempting to trigger full irrigation with the CTM method, a fixed DANS threshold of 0.5 °C was found to be more appropriate than the literature value of 1.0 °C, but consideration of crop growth stage would further improve scheduling.

Reconstruction and application of the temperature-vegetation-precipitation drought index in mainland China based on remote sensing datasets and a spatial distance model

In recent years, remote sensing drought monitoring indices have been gradually developed and have been widely used for global or regional drought monitoring due to their strong drought-monitoring capabilities and easy implementation advantages. However, some defects of remote sensing drought indices stand to be improved due to certain errors in the inversion of surface characteristics by remote sensing datasets. The temperature-vegetation-precipitation drought index (TVPDI) was taken as the research object, and the leaf area index (LAI), the difference between the land surface temperature (LST) and monthly average temperature, and Global Precipitation Measurement (GPM) precipitation data were selected instead of the normalized difference vegetation index (NDVI), LST and tropical rainfall measuring mission (TRMM) data to improve TVPDI. The improved remote sensing drought index was named the improved temperature-vegetation-precipitation drought index (iTVPDI). The drought-monitoring accuracy of iTVPDI was verified by the gross primary productivity (GPP), soil moisture, and crop yield per unit. The drought-monitoring ability of iTVPDI was verified with traditional drought indices, including the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), Palmer drought severity index (PDSI), temperature-vegetation drought index (TVDI), drought severity index (DSI) and crop water stress index (CWSI). The drought-monitoring accuracy of iTVPDI was verified by selecting sample areas. iTVPDI was applied to monitor drought in mainland China over the 2001–2020 period and obtained four main results. First, the correlation analyses of iTVPDI and TVPDI with GPP, crop yield per unit area, and soil moisture showed that iTVPDI had a stronger monitoring ability in Northeast, North, and Southwest China; the R2 value obtained with soil moisture was 0.62 (p < 0.05), and this value was higher than that of TVPDI. Then, the correlation analyses of iTVPDI and TVPDI with SPI, SPEI, PDSI, CWSI, DSI and TVDI showed that the correlation coefficients of iTVPDI and TVPDI with these six indicators were basically consistent, which indicated that the drought-monitoring capability of iTVPDI was consistent with that of TVPDI. In local areas such as the Qinghai-Tibet Plateau in China, the monitoring ability of iTVPDI was stronger than that of TVPDI. Third, through the sample area analysis, iTVPDI was found to moderate the NDVI-characterized vegetation factors in TVPDI in low-vegetation-cover areas affected by soil disturbances and in high-vegetation-cover areas affected by oversaturation. Finally, the results obtained from the application of iTVPDI in mainland China showed that during the warm-dry to warm-wet climate transition between 2001 and 2021, in 2010 and 2018, and in other special drought years, iTVPDI had the best response.

Crop response to thermal stress without yield loss in irrigated maize and soybean in Nebraska

Thermal sensing provides rapid and accurate estimation of crop water stress through canopy temperature data. Canopy temperature is highly dependent on the transpiration rate of the leaves. It is usually assumed that any reduction in crop evapotranspiration (ET) leads to crop yield loss. As a result, an increase in canopy temperature due to a decrease in crop ET would indicate crop yield loss. This research evaluated the hypothesis that crop water stress could be detected using canopy temperature measurements (increased leaf temperature) from infrared thermometers (IRTs) before incurring crop yield loss. This would be possible in a narrow range when the photosynthesis rate (and carbon assimilation) is limited by solar radiation (energy-limiting water stress) while the leaf has abundant carbon dioxide for photosynthesis. Once photosynthesis becomes limited by carbon dioxide (carbon-dioxide-limiting water stress), then yield reduction would occur. In this field experiment, measured response variables included the integrated crop water stress index (iCWSI), ET, and crop yield for maize and soybean during the 2020 and 2021 growing seasons. The irrigation was applied at four different refill levels: rainfed (0%), deficit (50%), full (100%), and over (150%). The irrigation depth was prescribed using four different irrigation methods. The field was irrigated with a center pivot irrigation system, which was also used as a platform to mount IRT sensors. The iCWSI thresholds required for irrigation management were determined using the iCWSI dataset collected in 2020. The low, medium, and high iCWSI thresholds were 120, 150, and 180, respectively for maize and 110, 130, and 150, respectively for soybean. These thresholds should be updated with iCWSI data from future studies in this region to increase the credibility of the thresholds for irrigation management. The mean iCWSI values for consecutive days after a wetting event substantially increased with time for each irrigation level and a larger range in iCWSI values was observed among the irrigation levels after three days from a wetting event. The seasonal iCWSI for different levels were found to be negatively correlated with seasonal evapotranspiration for both years. The correlations between seasonal ET and crop yield were significant with the rainfed and deficit levels for maize (p-value < 0.001) and soybean (p-value = 0.04) in 2020. The iCWSI and yield data for the fully watered plots indicated that thermal stress was detected using the sensing system without incurring yield loss (i.e., energy-limiting water stress). The ET and yield data for 2021 indicated that reduction in seasonal crop ET did not result in yield loss which also supported the hypothesis. Future studies should investigate whether this phenomenon of detecting crop water stress in an early stage without yield loss is observed in other climates and locations.

Assessment of the Combined Risk of Drought and High-Temperature Heat Wave Events in the North China Plain during Summer

Drought-induced risk has attracted the attention of many scholars, but the risk of combined events caused by drought and high-temperature heat waves still needs further study. Based on MODIS products and meteorological data, the spatiotemporal variation characteristics of summer drought and high-temperature heat waves in the North China Plain from 2000 to 2018 were analyzed by the standardized precipitation evapotranspiration index (SPEI), crop water stress index (CWSI) and high-temperature threshold, and their combined-events risk was evaluated. The results showed that (1) from 2000 to 2018, summer drought in the North China Plain became more severe. Especially in Henan, Anhui and Jiangsu Provinces, drought increased significantly. (2) From 2000 to 2018, the frequency and intensity of high-temperature heat wave events in the North China Plain gradually increased at rates of 0.28 times/10 year and 1.6 °C/10 year, respectively. (3) The slightly high risk and high risk caused by summer drought were mainly distributed in Hebei Province and Tianjin Municipality in the north, and the risk change was characterized by a decrease in the north and an increase in the south. (4) The combined-events risk of summer drought and high-temperature heat waves did not increase significantly, with an increase rate of approximately 0.01/10 year. Among them, the increase rate of combined-events risk in Henan Province was the largest (0.14/10 year), followed by the obvious increase in northern Anhui, Jiangsu and southern Shandong, while the risk in Beijing showed a decreasing trend. The research results have scientific guiding significance for formulating disaster prevention and reduction strategies.

Global spread of water scarcity risk through trade

Water scarcity is increasingly perceived as a great challenge to the sustainable development of human society. The economic output loss due to water scarcity is defined as water scarcity risk (WSR), which constitutes local water scarcity risk (LWSR, local economic output loss in water-using sectors due to water scarcity) and virtual water scarcity risk (VWSR, the spread of LWSR through trade systems). However, the traditional water stress index used in the existing WSR assessment research is limited in its accuracy and descriptiveness. Here we address these limitations by quantifying the LWSR with the consideration of environmental flow requirement, water intensity, economic output, and further assessing the VWSR. Results show that the WSR of the world is around 2.7 trillion US dollars in 2016. We also identify the hotspots of WSR at the national and sectoral levels. Our findings help lay the foundation for nations to develop strategies for mitigating WSR.

Automating reference temperature measurements for crop water stress index calculations: A case study on grapevines

The use of temperature as an indicator of water stress is gaining attention in agriculture. However, the need for reference temperature measurements in the field (Twet and Tdry) for normalization purposes (calculation of the crop water stress index - CWSI) inhibits the practical implementation of this technique. Therefore, in this study, two new models namely the heat transfer (HT) model and the empirical (EMP) model, are presented and compared with the standard method of physical reference temperature measurements and leaf energy balance calculation as an exploratory case study on grapevines. The HT model is a novel method, based on physical heat transfer principles and uses only input data obtained from a conventional weather station to determine reference temperatures. The EMP model is a simple method based on wet- and dry-bulb temperatures, calculated from ambient temperature and relative humidity measurements. To develop and evaluate the new models, physical measurements of the reference temperatures were taken in a commercial vineyard cv. Cabernet Sauvignon under different levels of water stress at two times during the day on seven days over the growing season. These physical measurements were used to optimize unknown parameters in the new methods by using the particle swarm optimization procedure. Input data for the model was collected by a conventional weather station located nearby the experimental vineyard block. In the validation process, it was found that the HT model can accurately predict the reference temperatures to within 0.5 °C and 1.0 °C for Twet and Tdry, respectively, reacting to environmental conditions as expected. The EMP model, requiring even less meteorological information, can accurately predict the reference temperatures to within 0.8 °C and 1.4 °C for Twet and Tdry, respectively. These proposed methods can provide reference temperatures that do not require physical measurements which can make the use of CWSI more practical and easier to implement for determining plant water stress in vineyards.

Application of Spectral Reflectance for Water Stress Detection of Corn (Zea mays L.) at Different Irrigation Treatments

Spectral reflectance indices have shown potential in crop water stress detection. Spectral reflectance was measured to determine vegetation indices, and its use as an indicator of water stress in corn under different irrigation treatments was explored. A completely randomized experimental design was conducted to detect water stress in potted IPB Var13 corn (Zea mays L.) under screenhouse conditions. Corn plants were subjected to three water stress treatments with three replications during the late vegetative (42 - 60 days after planting (DAP)) and reproductive (55 - 77 DAP) stages based on the total available water (TAW) in the soil: well-watered (&gt; 50% TAW), moderately water-stressed (35% -50% TAW), and severely water-stressed (20%-35% TAW). Hyperspectral vegetation indices computed from the leaf spectral reflectance measurements which include the Water Index and the Normalized Water Indices (NWI-1, NWI-2, NWI-3, and NWI-4) were calculated and correlated with leaf relative water content, Crop Water Stress Index, and grain yield. Results revealed that the vegetation indices considered for this study showed no significant response to the implemented water stress treatments for both sampling growth stages. However, it was found that NWI-3 (r = -0.88878073) and NWI-2 (r = -0.92222618) produced the most significant correlation with corn grain yield for the vegetative and reproductive stages, respectively. Hence, these spectral indices could be significantly used for early corn grain yield assessment during its late vegetative and reproductive stages.

ÍNDICE DE ESTRESSE HÍDRICO DO FEIJÃO-CAUPI EM DIFERENTES DISPONIBILIDADES HÍDRICAS EM CASTANHAL-PA

ABSTRACT Cowpea is a crop of great socioeconomic relevance for the populations of the North and Northeast of the country, and its low yield is commonly related to environmental stresses, especially water. The objective of this study was to evaluate the water stress index of cowpea, cultivar BR3 - Tracuateua, subjected to different irrigation levels (100, 50, 25 and 0% of ETc) in three reproductive phenological stages (R7, R8 and R9) in Castanhal-PA, Brazil. The experimental design was in randomized blocks, with six replications and four treatments corresponding to 100, 50, 25 and 0% of daily replacement of crop evapotranspiration, during the reproductive period, through an irrigation system. The surface temperature readings were made with infrared thermometer, during the reproductive stage. The smallest absolute temperature differences between canopy and air occurred in stages R7 and R8. The highest values of water stress index (CWSI) were verified when the plant was under water deficit, regardless of phenological stages. The effect of water deficit caused reductions in stomatal conductance of 58.82% (R7), 83.57% (R8) and 84.87% (R9), in leaf transpiration of 45.97% (R7), 64.21% (R8) and 65.90% (R9) and in the net photosynthetic rate of 40.75% (R7), 66.92% (R8) and 74% (R9). The CWSI varied with the availability of water, showing the highest value (0.75) in the treatment without irrigation, in the R8 stage. The CWSI proved to be a good indicator of the water status of the plant.

Challenging the water stress index concept: Thermographic assessment of Arabidopsis transpiration.

Water stress may greatly limit plant functionality and growth. Stomatal closure and consequently reduced transpiration are considered as early and sensitive plant responses to drought and salinity stress. An important consequence of stomatal closure under water stress is the rise of leaf temperature (T leaf), yet T leaf is not only fluctuating with stomatal closure. It is regulated by several plant parameters and environmental factors. Thermal imaging and different stress indices, incorporating actual leaf/crop temperature and reference temperatures, were developed in previous studies toward normalizing for effects unassociated to water stress on T leaf, aiming at a more efficient water stress assessment. The concept of stress indices has not been extensively studied on the model plant Arabidopsis thaliana. Therefore, the aim of this study was to examine the different indices employed in previous studies in assessing rosette transpiration rate (E) in Arabidopsis plants grown under two different light environments and subjected to salinity. After salinity imposition, E was gravimetrically quantified, and thermal imaging was employed to quantify rosette (T rosette) and artificial reference temperature (T wet, T dry). T rosette and several water stress indices were tested for their relation to E. Among the microclimatic growth conditions tested, RWSI1 ([T rosette − T wet]/[T dry − T wet]) and RWSI2 ([T dry − T rosette]/[T dry − T wet]) were well linearly‐related to E, irrespective of the light environment, while the sole use of either T wet or T dry in different combinations with T rosette returned less accurate results. This study provides evidence that selected combinations of T rosette, T dry, and T wet can be utilized to assess E under water stress irrespective of the light environment.