Abimbola et al., 2022 - Google Patents
Improving crop modeling to better simulate maize yield variability under different irrigation managementsAbimbola et al., 2022
View PDF- Document ID
- 13821969400742541201
- Author
- Abimbola O
- Franz T
- Rudnick D
- Heeren D
- Yang H
- Wolf A
- Katimbo A
- Nakabuye H
- Amori A
- Publication year
- Publication venue
- Agricultural Water Management
External Links
Snippet
Crop models have been used for investigating crop responses to environmental stresses for decades. The study objectives were to (i) calibrate and validate a simple crop model (Hybrid- Maize) using in-situ measured data from sixteen uniquely managed treatments as part of the …
- 238000003973 irrigation 0 title abstract description 44
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
- G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
- G06K9/00657—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas of vegetation
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Basso et al. | Seasonal crop yield forecast: Methods, applications, and accuracies | |
| Jongschaap | Run-time calibration of simulation models by integrating remote sensing estimates of leaf area index and canopy nitrogen | |
| Batchelor et al. | Examples of strategies to analyze spatial and temporal yield variability using crop models | |
| Betbeder et al. | Assimilation of LAI and dry biomass data from optical and SAR images into an agro-meteorological model to estimate soybean yield | |
| Fieuzal et al. | Estimation of corn yield using multi-temporal optical and radar satellite data and artificial neural networks | |
| Huang et al. | Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model | |
| Wang et al. | Estimating near future regional corn yields by integrating multi-source observations into a crop growth model | |
| Wagle et al. | Parameterizing ecosystem light use efficiency and water use efficiency to estimate maize gross primary production and evapotranspiration using MODIS EVI | |
| Gaso et al. | Predicting within-field soybean yield variability by coupling Sentinel-2 leaf area index with a crop growth model | |
| Gbegbelegbe et al. | Baseline simulation for global wheat production with CIMMYT mega-environment specific cultivars | |
| D’Urso et al. | Earth Observation products for operational irrigation management in the context of the PLEIADeS project | |
| Basso et al. | Landscape position and precipitation effects on spatial variability of wheat yield and grain protein in Southern Italy | |
| Zhang et al. | Estimating wheat yield by integrating the WheatGrow and PROSAIL models | |
| Thorp et al. | Integrating geospatial data and cropping system simulation within a geographic information system to analyze spatial seed cotton yield, water use, and irrigation requirements | |
| Belal et al. | Precision farming technologies to increase soil and crop productivity | |
| Han et al. | Linking an agro-meteorological model and a water cloud model for estimating soil water content over wheat fields | |
| Lin et al. | Methodological evolution of potato yield prediction: a comprehensive review | |
| Bandaru et al. | Geo-CropSim: A Geo-spatial crop simulation modeling framework for regional scale crop yield and water use assessment | |
| Fieuzal et al. | Forecast of wheat yield throughout the agricultural season using optical and radar satellite images | |
| Naab et al. | Adapting and evaluating the CROPGRO-peanut model for response to phosphorus on a sandy-loam soil under semi-arid tropical conditions | |
| Wu et al. | Global parameters sensitivity analysis of modeling water, energy and carbon exchange of an arid agricultural ecosystem | |
| Rozenstein et al. | Data-driven estimation of actual evapotranspiration to support irrigation management: Testing two novel methods based on an unoccupied aerial vehicle and an artificial neural network | |
| Solgi et al. | Remote sensing of canopy water status of the irrigated winter wheat fields and the paired anomaly analyses on the spectral vegetation indices and grain yields | |
| Della Nave et al. | Calibrating APSIM for forage sorghum using remote sensing and field data under sub-optimal growth conditions | |
| Abimbola et al. | Improving crop modeling to better simulate maize yield variability under different irrigation managements |