Spatial variability of the green water footprint using a medium-resolution remote sensing technique: The case of soybean production in the Southeast Argentine Pampas

Rodríguez, Paula Olivera; Holzman, Mauro; Degano, María Florencia; Faramiñán, Adán; Rivas, Raúl Eduardo; Bayala, Martín Ignacio

doi:10.1016/j.scitotenv.2020.142963

Cited by 21 publications

(7 citation statements)

References 51 publications

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“…They also encompass data across various time intervals, from hourly to daily, monthly, and yearly. Moreover, they make it feasible to examine changes and monitor terrestrial events using these data (Ge et al, 2019), including agricultural and natural resources management (Rodriguez et al, 2015), groundwater (Ollivier et al, 2021), water quality (Li et al, 2021), salinity management (Apan et al, 2004), and crop water requirement (Salehnia et al, 2018;Olivera Rodriguez et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of CRU TS, GPCC, AgMERRA, and AgCFSR meteorological datasets for estimating climate and crop variables: A case study of maize in Qazvin Province, Iran

2022

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In the past few decades, meteorological datasets from remote sensing techniques in agricultural and water resources management have been used by various researchers and managers. Based on the literature, meteorological datasets are not more accurate than synoptic stations, but their various advantages, such as spatial coverage, time coverage, accessibility, and free use, have made these techniques superior, and sometimes we can use them instead of synoptic stations. In this study, we used four meteorological datasets, including Climatic Research Unit gridded Time Series (CRU TS), Global Precipitation Climatology Centre (GPCC), Agricultural National Aeronautics and Space Administration Modern-Era Retrospective Analysis for Research and Applications (AgMERRA), Agricultural Climate Forecast System Reanalysis (AgCFSR), to estimate climate variables, i.e., precipitation, maximum temperature, and minimum temperature, and crop variables, i.e., reference evapotranspiration, irrigation requirement, biomass, and yield of maize, in Qazvin Province of Iran during 1980-2009. At first, data were gathered from the four meteorological datasets and synoptic station in this province, and climate variables were calculated. Then, after using the AquaCrop model to calculate the crop variables, we compared the results of the synoptic station and meteorological datasets. All the four meteorological datasets showed strong performance for estimating climate variables. AgMERRA and AgCFSR had more accurate estimations for precipitation and maximum temperature. However, their normalized root mean square error was inferior to CRU for minimum temperature. Furthermore, they were all very efficient for estimating the biomass and yield of maize in this province. For reference evapotranspiration and irrigation requirement CRU TS and GPCC were the most efficient rather than AgMERRA and AgCFSR. But for the estimation of biomass and yield, all the four meteorological datasets were reliable. To sum up, GPCC and AgCFSR were the two best datasets in this study. This study suggests the use of meteorological datasets in water resource management and agricultural management to monitor past changes and estimate recent trends.

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Section: Introductionmentioning

confidence: 99%

Evaluation of CRU TS, GPCC, AgMERRA, and AgCFSR meteorological datasets for estimating climate and crop variables: A case study of maize in Qazvin Province, Iran

2022

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“…With the development of new measurement technologies, remote sensing is widely used to estimate crop water consumption (Olivera Rodriguez et al., 2021). Many researchers (Dangwal et al., 2015; Hoffmann et al., 2016; Jackson et al., 1981; Y. J. Wang et al., 2014) have calculated crop water consumption based on remote sensing data and estimation equations, including the crop coefficient, energy balance, water balance (Vosselen et al., 2005), and water vapor diffusion (Tong et al., 2004) methods.…”

Section: Introductionmentioning

confidence: 99%

“…With the development of new measurement technologies, remote sensing is widely used to estimate crop water consumption (Olivera Rodriguez et al, 2021). Many researchers (Dangwal et al, 2015;Hoffmann et al, 2016;Jackson et al, 1981; Y. J.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Crop Water Demand and Consumption Considering Irrigation Effects Based on Coupled Hydrology‐Crop Growth Model

Zhang

Singh

et al. 2021

J Adv Model Earth Syst

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Hydrological models are widely used in regional drought assessments. Usually, the spatiotemporal development of droughts is evaluated only via drought indexes based on simulated soil moisture. However, the physiological status of crops is key to accurately assessing the effect of drought, and hydrological models either do not consider crop physiological status or oversimplify it. In addition, the effects of reservoirs, irrigation, and crop rotation are generally not considered. To overcome the oversimplification of crop and irrigation modules in hydrological models, the variable infiltration capacity (VIC) model was coupled with the Environmental Policy Integrated Climate model. Irrigation and reservoir modules were introduced into the coupled hydrology-crop growth (CHC) model. The CHC model was built for the watershed upstream of Heilin hydrological station on the Qingkou River on the border of Jiangsu and Shandong Provinces, China. The model performance was compared with simulation results of the Soil and Water Assessment Tool and VIC, which showed that the CHC model was more accurate. The temporal variation of water consumption and demand of three crop rotations, namely spring peanuts, summer peanuts-winter wheat, and summer corn-winter wheat, in the study area was analyzed. Drought area data obtained from Junan County yearbooks were consistent with the simulated water deficit and drought index considering the effect of irrigation. Crop irrigation increased soil water content and evapotranspiration and decreased crop water deficit. Therefore, crop irrigation was considered in hydrological simulations. The CHC model reliably simulated crop water consumption, soil moisture, and drought index, and the model simulated data could be used as basic data for water resources management and drought mitigation measures. Plain Language SummaryCommonly used hydrological models oversimplify the simulation of crop growth. Some studies have coupled hydrological and crop models, but have several limitations, for example, the effects of reservoirs, irrigation, and crop rotation are not considered. Therefore, crop water consumption and drought events have not been accurately simulated. To overcome this limitation, this study coupled the variable infiltration capacity (VIC) and Environmental Policy Integrated Climate models with irrigation, reservoir, and gridded routing modules. The coupled hydrology and crop growth (CHC) model was developed, and the accuracy of the CHC simulation results was higher than that of the Soil and Water Assessment Tool and VIC simulation results. The temporal variation of crop water consumption and demand along the Qingkou River, China, were simulated. In summer, meteorological and soil water conditions cause the crop water demand to be minimum to correspond to maximum water consumption, which generated the maximum water deficit. The simulated water deficit and drought index considering irrigation effects were consistent with the drought area data obtained from Junan County yearbooks. Irrigation incre...

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“…However, this scale does not allow spatial variations in agrochemical application rates, soil types, management practices and yields to be captured [22,23]. In consequence, the homogenisation of variables results in equally homogeneous L and GWF values, which may not be representative [16,24]. It also does not allow differentiation of the GWF in surface and groundwater [25].…”

Section: Introductionmentioning

confidence: 99%

Different Approaches to Assessing Pollution Load: The Case of Nitrogen-Related Grey Water Footprint of Barley and Soybean in Argentina

Rodríguez

Holzman

Mujica

et al. 2021

Water

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Agriculture is among the main causes of water pollution. Currently, 75% of global anthropogenic nitrogen (N) loads come from leaching/runoff from cropland. The grey water footprint (GWF) is an indicator of water resource pollution, which allows for the evaluation and monitoring of pollutant loads (L) that can affect water. However, in the literature, there are different approaches to estimating L and thus contrasting GWF estimates: (A1) leaching/runoff fraction approach, (A2) surplus approach and (A3) soil nitrogen balance approach. This study compares these approaches for the first time to assess which one is best adapted to real crop production conditions and optimises GWF calculation. The three approaches are applied to assess N-related GWF in barley and soybean. For barley in 2019, A3 estimated a GWF value 285 to 196% higher than A1, while in 2020, the A3 estimate was 135 to 81% higher. Soybean did not produce a GWF due to the crop characteristics. A3 incorporated N partitioning within the agroecosystem and considered different N inputs beyond fertilization, improving the accuracy of L and GWF estimation. Providing robust GWF results to decision-makers may help to prevent or reduce the impacts of activities that threaten the world’s water ecosystems and supply.

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Spatial variability of the green water footprint using a medium-resolution remote sensing technique: The case of soybean production in the Southeast Argentine Pampas

Cited by 21 publications

References 51 publications

Evaluation of CRU TS, GPCC, AgMERRA, and AgCFSR meteorological datasets for estimating climate and crop variables: A case study of maize in Qazvin Province, Iran

Evaluation of CRU TS, GPCC, AgMERRA, and AgCFSR meteorological datasets for estimating climate and crop variables: A case study of maize in Qazvin Province, Iran

Simulation of Crop Water Demand and Consumption Considering Irrigation Effects Based on Coupled Hydrology‐Crop Growth Model

Different Approaches to Assessing Pollution Load: The Case of Nitrogen-Related Grey Water Footprint of Barley and Soybean in Argentina

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