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4 results for “crop consumption”
CWFETB-China: Gridded dataset of consumptive water footprints, evaporation, transpiration, and associate benchmarks of crop production in China (2000-2018)
<p>The CWFETB-China is a 5-arcmin gridded dataset of monthly green and blue water footprint of crop production (WFCP), evaporation (E), transpiration (Tr), and associated unit WFCP benchmarks for 21 crops grown in China during 2000-2018. As compared to the existing gridded WFCP datasets, the CWFETB-China has four improvements: (i) It evaluated the effects of different water supply modes (irrigated or rain-fed) and irrigation practices (furrow, sprinkler, and micro-irrigation) on water consumption throughout the crop growth period. (ii) It distinguished between monthly blue and green water consumption via soil evaporation and crop transpiration. (iii) The dataset encompassed both the WFCP in m<sup>3 </sup>yr<sup>-1</sup> and the uWFCP in m<sup>3 </sup>ton<sup>-1</sup>. (iv) It identified uWFCP benchmarks that differentiated between various climatic zones and irrigation practices. The dataset is able to support for precise crop water productivity assessments, agricultural water-saving evaluations, the development of sustainable irrigation techniques, cropping structure optimisation, and crop-related interregional virtual water trade analysis.</p> <p> </p> <p>Format: NetCDF-4 (5 arcmin) or .xlsx files (benchmark data).</p> <p>Projected coordinate system: WGS 84</p>
Importance of non-consumptive effects of Nabis americoferus in biological control strategies including trap crops against the Tarnished plant bug, Lygus lineolaris
<p>Dataset on<span> polyphagous tarnished plant bug (TPB), <em>Lygus lineolaris </em>(Palisot de Beauvois) (Hemiptera: Miridae) </span><span> host preferences and reproductive behaviors under various integrated pest management approaches. </span></p>
Data from: Exploring movement decisions: can Bayesian movement-state models explain crop consumption behaviour in elephants (Loxodonta africana)?
Open the record for dataset details and reuse information.
Global spatially explicit crop water consumption shows an overall increase of 9% for 46 agricultural crops from 2010 to 2020: Data and software
<p>This dataset comprises spatial and temporal data related to our analysis on blue and green water consumption (WC) of global crop production in high spatial resolution (5 arc-minutes – approximately 10 km at the equator) for the years 2020, 2010 and 2000.</p> <p><strong>Modelling water consumption of SPAM data<br></strong></p> <p>We use SPAM (Spatial Production Allocation Model) data, released by the International Food Policy research Institute (IFPRI). We use SPAM2020 data for the year 2020 (46 crops), SPAM2010 data for the year 2010 (42 crops) and SPAM2000 data for the year 2000 (20 crops).</p> <p>We develop a Python-based global gridded crop green and blue WC assessment tool, entitled <em>CropGBWater. </em>Operating on a daily time scale, CropGBWater dynamically simulates rootzone water balance and related fluxes. We provide this model open access as <a href="https://zenodo.org/api/records/17059989/draft/files/Data_S10_CropGBWater_v02_1c-clean.ipynb/content">Data_S10</a> </p> <p>SPAM2020 crop data are modelled for the years 2018-2022, SPAM2010 crop data for the years 2008-2012 and SPAM2000 crop data for the years 1998-2002. We compute WCbl (blue WC) and WCgn (green WC), with components WCgn,irr (green WC of irrigated area) and WCgn,rf (green WC of rainfed area)<br><br><strong>File description:</strong></p> <p>The data-set consists of the following files:</p> <ul> <li>Data_S4: <a href="https://zenodo.org/records/15779747/files/Data_S4_Y2020_WC_m3_gridded.zip" target="_blank" rel="noopener">Data_S4_Y2020_WC_m3_gridded.zip</a><br>Folder with 46 individual crop grid files (5arc min resolution, with x & y coordinates), monthly and annual WCbl, WCgn,irr and WCgn,rf values in m3 in csv format, year 2020. Individual crop GIS-Rasters for annual m3 amounts are provided as Data_S17</li> <li>Data_S5: <a href="https://zenodo.org/records/15779747/files/Data_S5_Y2020_WC_mm_gridded.zip" target="_blank" rel="noopener">Data_S5_YR2020_WC_mm_gridded_csv</a><br>Folder with 46 individual crop grid files (5arc min resolution, with x & y coordinates), monthly and annual WCbl, WCgn,irr and WCgn, rf in mm as well as SPAM harvested area values in csv format, year 2020. Individual crop GIS-Rasters for annual mm amounts are provided as Data_S18</li> <li>Data_S6: <a href="https://zenodo.org/records/15779747/files/Data_S6_YR2020_WC_gridded_individual-crops-m3_annual.xlsx" target="_blank" rel="noopener">Data_S6_YR2020_WC_gridded_individual-crops-m3_annual.xlsx</a><br>One grid file (5arc min resolution, with x & y coordinates) with annual WCbl, WCgn,irr and WCgn, rf values in m3, differentiating between individual crops, year 2020. </li> <li>Data_S7: <a href="https://zenodo.org/records/15779747/files/Data_S7_YR2020_WC_gridded_sum-of-crops-m3_monthly-annual.csv" target="_blank" rel="noopener">Data_S7_YR2020_WC_gridded_sum-of-crops-m3_monthly-annual.csv</a><br>One grid file (5arc min resolution, with x & y coordinates) with monthly and annual WCbl, WCgn,irr and WCgn, rf values in m3, for the sum of all crops, year 2020</li> <li>Data_S8: <a href="https://zenodo.org/records/15779747/files/Data_S8_YR2000_WC_mm_m3_gridded.zip" target="_blank" rel="noopener">Data_S8_YR2000_WC_mm_m3_gridded.zip</a><br>Grid (5arc min resolution, with x & y coordinates) with annual WCbl, WCgn,irr and WCgn, rf values in mm and m3, as well as SPAM harvested area amounts, for each crop, year 2000</li> <li>Data_S9: <a href="https://zenodo.org/records/17059989/files/Data_S9_YR2010_WC_mm_m3_gridded.zip" target="_blank" rel="noopener">Data_S9_YR2010_WC_mm_m3_gridded.zip</a><br>Grid (5arc min resolution, with x & y coordinates) with annual WCbl, WCgn,irr and WCgn, rf values in mm and m3, as well as SPAM harvested area amounts, for each crop, year 2010</li> <li>Data_S10: <a href="https://zenodo.org/records/17059989/files/Data_S10_CropGBWater_v02_1c-clean.ipynb">Data_S10_CropGBWater_v02_1c-clean.ipynb</a> Python-based global gridded crop green and blue WC assessment tool, entitled <em>CropGBWater</em></li> </ul> <ul> <li>Data_S11: <a href="https://zenodo.org/records/15779747/files/Data_S11_YR2020_WC-mm_Rice_RiceAtlas.xlsx" target="_blank" rel="noopener">Data_S11_YR2020_WC-mm_Rice_RiceAtlas.xls</a><br>Grid (5arc min resolution, with x & y coordinates) with monthly and annual WCbl, WCgn,irr and WCgn, rf values in mm, for rice, year 2020, RiceAtlas calendar</li> <li>Data_S12: <a href="https://zenodo.org/records/15779747/files/Data_S12_INPUT_YR2020_cropcalendars.zip" target="_blank" rel="noopener">Data_S12_INPUT_YR2020_cropcalendars.zip</a><br>Modelling INPUT data for year 2020: Crop calendars</li> <li>Data_S13: <a href="https://zenodo.org/records/15779747/files/Data_S13_INPUT_YR2020_ET0.zip" target="_blank" rel="noopener">Data_S13_INPUT_YR2020_ET0.zip</a><br>Modelling INPUT data for year 2020: daily ET0</li> <li>Data_S14: <a href="https://zenodo.org/records/15779747/files/Data_S14_INPUT_YR2020_Precip.zip" target="_blank" rel="noopener">Data_S14_INPUT_YR2020_Precip.zip</a><br>Modelling INPUT data for year 2020: daily Precipitation</li> <li>Data_S15: <a href="https://zenodo.org/records/15779747/files/Soil.zip" target="_blank" rel="noopener">Data_S15_INPUT_YR2020_Soil.zip</a><br>Modelling INPUT data for year 2020: Soil</li> <li>Data_S16: <a href="https://zenodo.org/records/15779747/files/Data_S16_INPUT_YR2020_Climate-SPAM-grid_&_Scripts.zip" target="_blank" rel="noopener">Data_S16_INPUT_YR2020_Climate-SPAM-grid_&_Scripts.zip</a><br>Modelling INPUT for year 2020: Climate-SPAM-grid and different processing scripts</li> <li>Data_S17: <a href="https://zenodo.org/records/15779747/files/Data_S17_Y2020_WC_m3_GisRasters.zip" target="_blank" rel="noopener">Data_S17_Y2020_WC_m3_GisRasters</a><br>GIS-Rasters of individual crops for year 2020 (as well as the sum of all crops), values in m3 per year, differentiation between WCbl, WCgn,irr and WCgn</li> <li>Data_S18: <a href="https://zenodo.org/records/15779747/files/Data_S18_Y2020_WC_mm_GisRasters.zip" target="_blank" rel="noopener">Data_S18_Y2020_WC_mm_GisRasters</a><br>GIS-Rasters of individual crops for year 2020, values in mm per year, differentiation between WCbl, WCgn,irr and WCgn</li> </ul> <p><em>Please only use the latest version of this zenodo repository</em></p> <p><strong>Publication:</strong></p> <p>For all details, please refer to the open access paper:</p> <p>Chukalla, A.D., Mekonnen, M.M., Gunathilake, D., Wolkeba, F.T., Gunasekara, B., Vanham, D. (2025) Global spatially explicit crop water consumption shows an overall increase of 9% for 46 agricultural crops from 2010 to 2020, Nature Food, Volume 6, <a href="https://doi.org/10.1038/s43016-025-01231-x" target="_blank" rel="noopener">https://doi.org/10.1038/s43016-025-01231-x</a></p> <p><strong>Funding:</strong></p> <p>This research, led by IWMI, a CGIAR centre, was carried out under the CGIAR Initiative on Foresight (<a href="www.cgiar.org/initiative/foresight/" target="_blank" rel="noopener">www.cgiar.org/initiative/foresight/</a>) as well as the CGIAR “Policy innovations” Science Program (<a href="www.cgiar.org/cgiar-research-porfolio-2025-2030/policy-innovations" target="_blank" rel="noopener">www.cgiar.org/cgiar-research-porfolio-2025-2030/policy-innovations</a>). The authors would like to thank all funders who supported this research through their contributions to the CGIAR Trust Fund (<a href="www.cgiar.org/funders" target="_blank" rel="noopener">www.cgiar.org/funders</a>).</p> <p><strong> </strong></p>
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