{"type": "FeatureCollection", "features": [{"id": "10.1016/j.fcr.2021.108182", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:15:32Z", "type": "Journal Article", "created": "2021-05-25", "title": "Global sensitivity analysis of crop yield and transpiration from the FAO-AquaCrop model for dryland environments", "description": "Open AccessPeer reviewed", "keywords": ["2. Zero hunger", "570", "Yield", "0208 environmental biotechnology", "0207 environmental engineering", "02 engineering and technology", "15. Life on land", "630", "AquaCrop", "6. Clean water", "Transpiration", "Dryland", "13. Climate action", "Sensitivity analysis"]}, "links": [{"href": "https://eprints.soton.ac.uk/449637/1/AquaCrop_GSA_rev2.pdf"}, {"href": "https://eprints.soton.ac.uk/449637/2/Lu2021_AquaCrop_GSA.pdf"}, {"href": "https://doi.org/10.1016/j.fcr.2021.108182"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Field%20Crops%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.fcr.2021.108182", "name": "item", "description": "10.1016/j.fcr.2021.108182", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.fcr.2021.108182"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-01T00:00:00Z"}}, {"id": "10.1016/j.agwat.2020.106585", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:15:08Z", "type": "Journal Article", "created": "2020-10-18", "title": "Parameterization of the AquaCrop model for simulating table grapes growth and water productivity in an arid region of Mexico.", "description": "Abstract Currently, the AquaCrop model has been widely tested for many fruit/grain crops; root and tuber crops; leafy vegetables, or forage crops, but is restricted to annual herbaceous species, while deciduous crops have received less if no attention. In this context, this study aims to test for the first time the ability of the AquaCrop model to simulate canopy cover (CC), actual evapotranspiration (ETa), total soil water content (TWC), biomass (B) and fruit yield (FY) of table grapes vineyards (Vitis vinifera L., cvs. Perlette and Superior) at the Costa de Hermosillo, Sonora in Northwest Mexico. Observed weather and soil physical parameters, with measured crop parameters from an experiment conducted during 2005 were used to develop climate, soil and crop input files for AquaCrop and for calibrating the model. While collected data during the 2006 growing season were used to validate the model.\u00a0The model adequately simulated CC, ETa and TWC during 2005 and 2006. The Root Mean Square Error (RMSE) between observed and measured CC, ETa and TWC were 5.18%, 0.46\u00a0mm/day and 10.11\u00a0mm during 2005, and 8.82%, 0.84\u00a0mm/day and 9.1\u00a0mm during 2006, respectively. The good accuracy of simulations of CC, ETa and TWC by the model have been confirmed by additional statistical parameters like the coefficient of determination (R2), The Mean Bias Error (MBE), the Willmott\u2019s index of agreement (d) and the Nash\u2013Sutcliffe Efficiency (NSE). For the B and FY simulations, the results showed that the model correctly reproduced the B and FY with NRMSE value of 8.8%. The estimated average value of FY (14.56\u00a0t/ha) for both seasons are in the range of the potential yield (14\u201318\u00a0t/ha) of table grapes in the irrigated Costa de Hermosillo in northwest Mexico. After the validation of the AquaCrop model, it was used to evaluate the irrigation scheduling by the farmer as well as to assess the water productivity computed as the ratio of crop production to crop water use. The results showed that, the recommended irrigation by the model was about 547\u00a0mm and 509\u00a0mm, which it is about half of that applied by the farmer (1006\u00a0mm and 929\u00a0mm) during 2005 and 2006, respectively. This large difference, which represents approximately 54% and 57% of the irrigation supply, is lost through deep percolation and could be saved without vegetation suffering from water stress while maintaining the same yield. The high loss of water by percolation affects significantly the water productivity (WP), which decreases from 3.22 to 1.74\u00a0kg/m3 if we consider the transpiration (WPTr), and the sum of ETa and Percolation (WPETa+Pr) for WP computations, respectively. Consequently, the AquaCrop model can be used as an operational tool by decision makers and growers to improve irrigation management. This is of crucial importance in arid and semi-arid regions where water is becoming increasingly scarce.", "keywords": ["0106 biological sciences", "2. Zero hunger", "550", "Evapotranspiration", "Water productivity", "[SPI] Engineering Sciences [physics]", "Percolation", "04 agricultural and veterinary sciences", "15. Life on land", "Tablegrapes (Vitisvinifera L. cvs. Perletteand Superior)", "01 natural sciences", "630", "Irrigation scheduling", "AquaCrop", "[SPI]Engineering Sciences [physics]", "Vitisvinifera L cvs Perletteand Superior", "0401 agriculture", " forestry", " and fisheries", "Tablegrapes"]}, "links": [{"href": "https://doi.org/10.1016/j.agwat.2020.106585"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20Water%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agwat.2020.106585", "name": "item", "description": "10.1016/j.agwat.2020.106585", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agwat.2020.106585"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-01T00:00:00Z"}}, {"id": "10.1016/j.rse.2023.113621", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:15:57Z", "type": "Journal Article", "created": "2023-05-13", "title": "Optimisation of AquaCrop backscatter simulations using Sentinel-1 observations", "description": "In preparation for active microwave-based data assimilation into a crop modeling system, the mapping of daily 1-km AquaCrop model (v6.1) biomass and surface soil moisture to backscatter was optimised, using two forward operators, i.e. the Water Cloud Model (WCM) and the Support Vector Regression (SVR). Both forward operators were calibrated (2014\u20132018) with 1-km Sentinel-1 backscatter ( ) observations in VV and VH polarisation, for three different study domains in Europe. For the validation period (2019\u20132021), the simulations showed reasonable performances around Czech Republic and the Iberian Peninsula, to good performances over Belgium, but with strong variations within each domain. The domain-averaged root mean square difference between the model and Sentinel-1 remained below 2 dB for both forward operators and all three study domains, and the mean bias for VV remained close to 0 dB, and close 0.5 dB for the VH polarisation. The WCM and SVR performed better in VV than VH and overall the SVR performed slightly better in mapping the AquaCrop soil moisture and vegetation to backscatter than the WCM. Additionally, the assumed linear relationship in the WCM between soil moisture and soil holds better for VV than for VH. The remaining differences between WCM or SVR simulations and Sentinel-1 observations are mainly caused by AquaCrop model errors.", "keywords": ["Agriculture and Food Sciences", "Crop biomass", "YIELD RESPONSE", "ASSIMILATION", "Backscatter modeling", "LEAF-AREA INDEX", "RADAR BACKSCATTER", "BIOMASS", "SAR BACKSCATTER", "AquaCrop optimisation", "13. Climate action", "SURFACE SOIL-MOISTURE", "Earth and Environmental Sciences", "SUPPORT", "Sentinel-1", "WATER", "Soil moisture", "FAO CROP MODEL"]}, "links": [{"href": "https://doi.org/10.1016/j.rse.2023.113621"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing%20of%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.rse.2023.113621", "name": "item", "description": "10.1016/j.rse.2023.113621", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.rse.2023.113621"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-08-01T00:00:00Z"}}, {"id": "10.1029/2024jg008231", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:16:42Z", "type": "Journal Article", "created": "2024-10-17", "title": "Assimilation of Sentinel\u20101 Backscatter to Update AquaCrop Estimates of Soil Moisture and Crop Biomass", "description": "Abstract

This study assesses the potential of regional microwave backscatter data assimilation (DA) in AquaCrop for the first time, using NASA's Land Information System. The objective is to assess whether the assimilation setup can improve surface soil moisture (SSM) and crop biomass estimates. SSM and crop biomass simulations from AquaCrop were updated using Sentinel\uffe2\uff80\uff901 synthetic aperture radar observations, over three regions in Europe in two separate DA experiments. The first experiment concerned updating SSM using VV\uffe2\uff80\uff90polarized backscatter and the corrections were propagated via the model to the biomass. In the second experiment, the DA setup was extended by also updating the biomass with VH\uffe2\uff80\uff90polarized backscatter. SSM was evaluated with local in situ data and with downscaled Soil Moisture Active Passive (SMAP) retrievals for all cropland grid cells, whereas crop biomass was compared to SMAP vegetation optical depth and the Copernicus dry matter productivity. The assimilation showed mixed results for root mean square error and Pearson's correlation, with slight overall improvements in the (anomaly) correlations of updated SSM relative to independent in situ and satellite data. By contrast, the biomass estimates obtained with backscatter DA did not agree better with reference data sets. Overall, the SSM evaluation showed that there is potential in using Sentinel\uffe2\uff80\uff901 backscatter for assimilation in AquaCrop, but the present setup was not able to improve crop biomass estimates. Our study reveals how the complex interaction between SSM, crop biomass and backscatter affect the impact and performance of DA, offering insight into ways to optimize DA for crop growth estimation.Process-based crop models such as AquaCrop are useful for a variety of applications but must be accurately calibrated and validated. Sugar beet is an important crop that is grown in regions under water scarcity. The discrepancies and uncertainty in past published calibrations, together with important modifications in the program, deemed it necessary to conduct a study aimed at the calibration of AquaCrop (version 6.1) using the results of a single deficit irrigation experiment. The model was validated with additional data from eight farms differing in location, years, varieties, sowing dates, and irrigation. The overall performance of AquaCrop for simulating canopy cover, biomass, and final yield was accurate (RMSE = 11.39%, 2.10 t ha\uffe2\uff88\uff921, and 0.85 t ha\uffe2\uff88\uff921, respectively). Once the model was properly calibrated and validated, a scenario analysis was carried out to assess the crop response in terms of yield and water productivity to different irrigation water allocations in the two main production areas of sugar beet in Spain (spring and autumn sowing). The results highlighted the potential of the model by showing the important impact of irrigation water allocation and sowing time on sugar beet production and its irrigation water productivity.

", "keywords": ["2. Zero hunger", "Water productivity", "Sugar beet", "sugar beet", "04 agricultural and veterinary sciences", "15. Life on land", "calibration", "irrigation water allocation", "Modelling", "AquaCrop", "6. Clean water", "Irrigation water allocation", "modelling", "Calibration", "water productivity", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2073-4441/11/9/1918/pdf"}, {"href": "https://www.mdpi.com/2073-4441/11/9/1918/pdf"}, {"href": "https://doi.org/10.3390/w11091918"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/w11091918", "name": "item", "description": "10.3390/w11091918", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/w11091918"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-09-14T00:00:00Z"}}, {"id": "10.3390/w14081188", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:19:15Z", "type": "Journal Article", "created": "2022-04-10", "title": "Estimating Yield from NDVI, Weather Data, and Soil Water Depletion for Sugar Beet and Potato in Northern Belgium", "description": "

Crop-yield models based on vegetation indices such as the normalized difference vegetation index (NDVI) have been developed to monitor crop yield at higher spatial and temporal resolutions compared to agricultural statistical data. We evaluated the model performance of NDVI-based random forest models for sugar beet and potato farm yields in northern Belgium during 2016\u20132018. We also evaluated whether weather variables and root-zone soil water depletion during the growing season improved the model performance. The NDVI integral did not explain early and late potato yield variability and only partly explained sugar-beet yield variability. The NDVI series of early and late potato crops were not sensitive enough to yield affecting weather and soil water conditions. We found that water-saturated conditions early in the growing season and elevated temperatures late in the growing season explained a large part of the sugar-beet and late-potato yield variability. The NDVI integral in combination with monthly precipitation, maximum temperature, and root-zone soil water depletion during the growing season explained farm-scale sugar beet (R2 = 0.84, MSE = 48.8) and late potato (R2 = 0.56, MSE = 57.3) yield variability well from 2016 to 2018 in northern Belgium.

", "keywords": ["AquaCrop-OSPy", "STRESS", "root-zone soil water depletion; AquaCrop-OSPy; sugar beet; potato; crop yield; NDVI; Belgium; weather impact; random forest", "NDVI", "Environmental Sciences & Ecology", "root-zone soil water depletion", "01 natural sciences", "Belgium", "INDEX", "0105 earth and related environmental sciences", "2. Zero hunger", "Science & Technology", "PRODUCTIVITY", "CROP", "sugar beet", "weather impact", "04 agricultural and veterinary sciences", "crop yield", "WINTER-WHEAT", "15. Life on land", "MODEL", "Physical Sciences", "Water Resources", "potato", "0401 agriculture", " forestry", " and fisheries", "Life Sciences & Biomedicine", "Environmental Sciences", "random forest"]}, "links": [{"href": "http://www.mdpi.com/2073-4441/14/8/1188/pdf"}, {"href": "https://www.mdpi.com/2073-4441/14/8/1188/pdf"}, {"href": "https://doi.org/10.3390/w14081188"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/w14081188", "name": "item", "description": "10.3390/w14081188", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/w14081188"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-08T00:00:00Z"}}, {"id": "10.5281/zenodo.6760976", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:20:52Z", "type": "Dataset", "title": "Net irrigation requirement under different climate scenarios using AquaCrop over Europe", "description": "This repository contains the setup and data related to the peer-reviewed article 'Net irrigation requirement under different climate scenarios using AquaCrop over Europe' accepted for HESS (https://hess.copernicus.org/preprints/hess-2021-631/). The README.txt file contains all information about the repository. Please contact Louise Busschaert (louise.busschaert@kuleuven.be) or Gabrielle De Lannoy (gabrielle.delannoy@kuleuven.be) for any further questions.", "keywords": ["irrigation requirement", "climate change", "regional AquaCrop", "crop modelling"], "contacts": [{"organization": "Busschaert, Louise, de Roos, Shannon, Thiery, Wim, Raes, Dirk, De Lannoy, Gabrielle J. M.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6760976"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6760976", "name": "item", "description": "10.5281/zenodo.6760976", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6760976"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-27T00:00:00Z"}}, {"id": "10.5281/zenodo.6760977", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:20:52Z", "type": "Dataset", "title": "Net irrigation requirement under different climate scenarios using AquaCrop over Europe", "description": "This repository contains the setup and data related to the peer-reviewed article 'Net irrigation requirement under different climate scenarios using AquaCrop over Europe' accepted for HESS (https://hess.copernicus.org/preprints/hess-2021-631/). The README.txt file contains all information about the repository. Please contact Louise Busschaert (louise.busschaert@kuleuven.be) or Gabrielle De Lannoy (gabrielle.delannoy@kuleuven.be) for any further questions.", "keywords": ["irrigation requirement", "climate change", "regional AquaCrop", "crop modelling"], "contacts": [{"organization": "Busschaert, Louise, de Roos, Shannon, Thiery, Wim, Raes, Dirk, De Lannoy, Gabrielle J. M.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6760977"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6760977", "name": "item", "description": "10.5281/zenodo.6760977", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6760977"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-27T00:00:00Z"}}, {"id": "10.5281/zenodo.4770737", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:20:47Z", "type": "Dataset", "title": "source code and datasets for gmd-2021-98", "description": "This repository contains data and code that was used to run and evaluate a spatially distributed version of the AquaCrop model. A preprint manuscript is currently under review with DOI: https://doi.org/10.5194/gmd-2021-98. The authors kindly request to not use this data before official publication of the manuscript. Please see the readme file for more detailed information about the dataset.", "keywords": ["AquaCrop", " regional crop model"], "contacts": [{"organization": "de Roos, Shannon, De Lannoy, Gabrielle, Raes, Dirk,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4770737"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4770737", "name": "item", "description": "10.5281/zenodo.4770737", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4770737"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-18T00:00:00Z"}}, {"id": "10.5281/zenodo.4770738", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:20:47Z", "type": "Dataset", "title": "source code and datasets for gmd-2021-98", "description": "This repository contains data and code that was used to run and evaluate a spatially distributed version of the AquaCrop model. A preprint manuscript is currently under review with DOI: https://doi.org/10.5194/gmd-2021-98. The authors kindly request to not use this data before official publication of the manuscript. Please see the readme file for more detailed information about the dataset.", "keywords": ["AquaCrop", " regional crop model"], "contacts": [{"organization": "de Roos, Shannon, De Lannoy, Gabrielle, Raes, Dirk,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4770738"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4770738", "name": "item", "description": "10.5281/zenodo.4770738", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4770738"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-18T00:00:00Z"}}, {"id": "10261/205841", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:21:39Z", "type": "Journal Article", "created": "2019-09-16", "title": "Modeling Sugar Beet Responses to Irrigation with AquaCrop for Optimizing Water Allocation", "description": "

Process-based crop models such as AquaCrop are useful for a variety of applications but must be accurately calibrated and validated. Sugar beet is an important crop that is grown in regions under water scarcity. The discrepancies and uncertainty in past published calibrations, together with important modifications in the program, deemed it necessary to conduct a study aimed at the calibration of AquaCrop (version 6.1) using the results of a single deficit irrigation experiment. The model was validated with additional data from eight farms differing in location, years, varieties, sowing dates, and irrigation. The overall performance of AquaCrop for simulating canopy cover, biomass, and final yield was accurate (RMSE = 11.39%, 2.10 t ha\u22121, and 0.85 t ha\u22121, respectively). Once the model was properly calibrated and validated, a scenario analysis was carried out to assess the crop response in terms of yield and water productivity to different irrigation water allocations in the two main production areas of sugar beet in Spain (spring and autumn sowing). The results highlighted the potential of the model by showing the important impact of irrigation water allocation and sowing time on sugar beet production and its irrigation water productivity.

", "keywords": ["2. Zero hunger", "Water productivity", "Sugar beet", "sugar beet", "04 agricultural and veterinary sciences", "15. Life on land", "calibration", "irrigation water allocation", "Modelling", "AquaCrop", "6. Clean water", "Irrigation water allocation", "modelling", "Calibration", "water productivity", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2073-4441/11/9/1918/pdf"}, {"href": "https://www.mdpi.com/2073-4441/11/9/1918/pdf"}, {"href": "https://doi.org/10261/205841"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10261/205841", "name": "item", "description": "10261/205841", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10261/205841"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-09-14T00:00:00Z"}}, {"id": "10396/18990", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:21:46Z", "type": "Journal Article", "created": "2019-09-16", "title": "Modeling Sugar Beet Responses to Irrigation with AquaCrop for Optimizing Water Allocation", "description": "

Process-based crop models such as AquaCrop are useful for a variety of applications but must be accurately calibrated and validated. Sugar beet is an important crop that is grown in regions under water scarcity. The discrepancies and uncertainty in past published calibrations, together with important modifications in the program, deemed it necessary to conduct a study aimed at the calibration of AquaCrop (version 6.1) using the results of a single deficit irrigation experiment. The model was validated with additional data from eight farms differing in location, years, varieties, sowing dates, and irrigation. The overall performance of AquaCrop for simulating canopy cover, biomass, and final yield was accurate (RMSE = 11.39%, 2.10 t ha\u22121, and 0.85 t ha\u22121, respectively). Once the model was properly calibrated and validated, a scenario analysis was carried out to assess the crop response in terms of yield and water productivity to different irrigation water allocations in the two main production areas of sugar beet in Spain (spring and autumn sowing). The results highlighted the potential of the model by showing the important impact of irrigation water allocation and sowing time on sugar beet production and its irrigation water productivity.

", "keywords": ["2. Zero hunger", "Water productivity", "Sugar beet", "sugar beet", "04 agricultural and veterinary sciences", "15. Life on land", "calibration", "irrigation water allocation", "Modelling", "AquaCrop", "6. Clean water", "Irrigation water allocation", "modelling", "Calibration", "water productivity", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2073-4441/11/9/1918/pdf"}, {"href": "https://www.mdpi.com/2073-4441/11/9/1918/pdf"}, {"href": "https://doi.org/10396/18990"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10396/18990", "name": "item", "description": "10396/18990", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10396/18990"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-09-14T00:00:00Z"}}, {"id": "10754/669278", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:21:50Z", "type": "Journal Article", "created": "2021-05-24", "title": "Global sensitivity analysis of crop yield and transpiration from the FAO-AquaCrop model for dryland environments", "description": "Project Co-ordinators: Dr. Jose Alfonso G\u00f3mez Calero (Instituto de Agricultura Sostenible (IAS-CISC), Dr. Weifeng Xu (Fujian Agriculture and Forest University, FAFU). -- Trabajo desarrollado bajo la financiaci\u00f3n del proyecto \u201cSoil Hydrology research platform underpinning innovation to manage water scarcity in European and Chinese cropping Systems\u201d (773903), coordinado por Jos\u00e9 Alfonso G\u00f3mez Calero, investigador del Instituto de Agricultura Sostenible (IAS). The application of crop models towards improved local scale prediction and precision management requires the identification and description of the major factors influencing model performance. Such efforts are particularly important for dryland areas which face rapid population growth and increasing constraints on water supplies. In this study, a global sensitivity analysis on crop yield and transpiration was performed for 49 parameters in the FAO-AquaCrop model (version 6.0) across three dryland farming areas with different climatic conditions. The Morris screening method and the variance-based Extended Fourier Amplitude Sensitivity Test (EFAST) method were used to evaluate the parameter sensitivities of several staple crops (maize, soybean or winter wheat) under dry, normal and wet scenarios. Results suggest that parameter sensitivities vary with the target model output (e.g., yield, transpiration) and the wetness condition. By synthesizing parameter sensitivities under different scenarios, the key parameters affecting model performance under both high and low water stress were identified for the three crops. Overall, factors relevant to root development tended to have large impacts under high water stress, while those controlling maximum canopy cover and senescence were more influential under low water stress. Parameter sensitivities were also shown to be stage-dependent from a day-by-day analysis of canopy cover and biomass simulations. Subsequent comparison with AquaCrop version 5.0 suggests that AquaCrop version 6.0 is less sensitive to uncertainties in soil properties. This work was partly funded through the \u2018A new paradigm in precision agriculture: assimilation of ultra-fine resolution data into a crop-yield forecasting model\u2019 project, supported by the King Abdullah University of Science and Technology, grant number OSR-2017-CRG6, and through the \u2018Building REsearch Capacity for sustainable water and food security In drylands of sub-saharan Africa (BRECcIA)\u2019 project, which is supported by UK Research and Innovation as part of the Global Challenges Research Fund, grant number NE/P021093/1. Matthew McCabe was funded by KAUST. G. De Lannoy was funded by EU project SHui GA 773903. Peer reviewed", "keywords": ["2. Zero hunger", "570", "Yield", "0208 environmental biotechnology", "0207 environmental engineering", "02 engineering and technology", "15. Life on land", "630", "AquaCrop", "6. Clean water", "Transpiration", "Dryland", "13. Climate action", "Sensitivity analysis"]}, "links": [{"href": "https://eprints.soton.ac.uk/449637/1/AquaCrop_GSA_rev2.pdf"}, {"href": "https://eprints.soton.ac.uk/449637/2/Lu2021_AquaCrop_GSA.pdf"}, {"href": "https://doi.org/10754/669278"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Field%20Crops%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10754/669278", "name": "item", "description": "10754/669278", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10754/669278"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-01T00:00:00Z"}}, {"id": "1854/LU-01JKX1Z1QJK1BHR9JV20HBZ5Z4", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:22:04Z", "type": "Journal Article", "created": "2023-05-13", "title": "Optimisation of AquaCrop backscatter simulations using Sentinel-1 observations", "description": "Open AccessIn preparation for active microwave-based data assimilation into a crop modeling system, the mapping of daily 1-km AquaCrop model (v6.1) biomass and surface soil moisture to backscatter was optimised, using two forward operators, i.e. the Water Cloud Model (WCM) and the Support Vector Regression (SVR). Both forward operators were calibrated (2014\u20132018) with 1-km Sentinel-1 backscatter (\u03d2\u00b0) observations in VV and VH polarisation, for three different study domains in Europe. For the validation period (2019\u20132021), the \u03d2\u00b0 simulations showed reasonable performances around Czech Republic and the Iberian Peninsula, to good performances over Belgium, but with strong variations within each domain. The domain-averaged root mean square difference between the model and Sentinel-1 \u03d2\u00b0 remained below 2 dB for both forward operators and all three study domains, and the mean bias for VV remained close to 0 dB, and close 0.5 dB for the VH polarisation. The WCM and SVR performed better in VV than VH and overall the SVR performed slightly better in mapping the AquaCrop soil moisture and vegetation to backscatter than the WCM. Additionally, the assumed linear relationship in the WCM between soil moisture and soil \u03d2\u00b0 holds better for VV than for VH. The remaining differences between WCM or SVR simulations and Sentinel-1 observations are mainly caused by AquaCrop model errors.", "keywords": ["Agriculture and Food Sciences", "Technology", "ASSIMILATION", "Sentine;-1", "Environmental Sciences & Ecology", "Geological & Geomatics Engineering", "BIOMASS", "Remote Sensing", "SAR BACKSCATTER", "SURFACE SOIL-MOISTURE", "SUPPORT", "0909 Geomatic Engineering", "WATER", "FAO CROP MODEL", "Imaging Science & Photographic Technology", "crop biomass", "Crop biomass", "YIELD RESPONSE", "Science & Technology", "backscatter modelling", "Backscatter modeling", "LEAF-AREA INDEX", "RADAR BACKSCATTER", "37 Earth sciences", "AquaCrop optimisation", "13. Climate action", "Earth and Environmental Sciences", "Sentinel-1", "Soil moisture", "0406 Physical Geography and Environmental Geoscience", "Life Sciences & Biomedicine", "Environmental Sciences"]}, "links": [{"href": "https://biblio.vub.ac.be/vubirfiles/112110259/108189295.pdf"}, {"href": "https://doi.org/1854/LU-01JKX1Z1QJK1BHR9JV20HBZ5Z4"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing%20of%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1854/LU-01JKX1Z1QJK1BHR9JV20HBZ5Z4", "name": "item", "description": "1854/LU-01JKX1Z1QJK1BHR9JV20HBZ5Z4", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-01JKX1Z1QJK1BHR9JV20HBZ5Z4"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-08-01T00:00:00Z"}}, {"id": "1854/LU-01JM1T576ZX50W7293M9RBH0RG", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-20T16:22:04Z", "type": "Journal Article", "created": "2024-10-17", "title": "Assimilation of Sentinel\u20101 Backscatter to Update AquaCrop Estimates of Soil Moisture and Crop Biomass", "description": "Abstract

This study assesses the potential of regional microwave backscatter data assimilation (DA) in AquaCrop for the first time, using NASA's Land Information System. The objective is to assess whether the assimilation setup can improve surface soil moisture (SSM) and crop biomass estimates. SSM and crop biomass simulations from AquaCrop were updated using Sentinel\uffe2\uff80\uff901 synthetic aperture radar observations, over three regions in Europe in two separate DA experiments. The first experiment concerned updating SSM using VV\uffe2\uff80\uff90polarized backscatter and the corrections were propagated via the model to the biomass. In the second experiment, the DA setup was extended by also updating the biomass with VH\uffe2\uff80\uff90polarized backscatter. SSM was evaluated with local in situ data and with downscaled Soil Moisture Active Passive (SMAP) retrievals for all cropland grid cells, whereas crop biomass was compared to SMAP vegetation optical depth and the Copernicus dry matter productivity. The assimilation showed mixed results for root mean square error and Pearson's correlation, with slight overall improvements in the (anomaly) correlations of updated SSM relative to independent in situ and satellite data. By contrast, the biomass estimates obtained with backscatter DA did not agree better with reference data sets. Overall, the SSM evaluation showed that there is potential in using Sentinel\uffe2\uff80\uff901 backscatter for assimilation in AquaCrop, but the present setup was not able to improve crop biomass estimates. Our study reveals how the complex interaction between SSM, crop biomass and backscatter affect the impact and performance of DA, offering insight into ways to optimize DA for crop growth estimation.

Process-based crop models such as AquaCrop are useful for a variety of applications but must be accurately calibrated and validated. Sugar beet is an important crop that is grown in regions under water scarcity. The discrepancies and uncertainty in past published calibrations, together with important modifications in the program, deemed it necessary to conduct a study aimed at the calibration of AquaCrop (version 6.1) using the results of a single deficit irrigation experiment. The model was validated with additional data from eight farms differing in location, years, varieties, sowing dates, and irrigation. The overall performance of AquaCrop for simulating canopy cover, biomass, and final yield was accurate (RMSE = 11.39%, 2.10 t ha\u22121, and 0.85 t ha\u22121, respectively). Once the model was properly calibrated and validated, a scenario analysis was carried out to assess the crop response in terms of yield and water productivity to different irrigation water allocations in the two main production areas of sugar beet in Spain (spring and autumn sowing). The results highlighted the potential of the model by showing the important impact of irrigation water allocation and sowing time on sugar beet production and its irrigation water productivity.

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