{"type": "FeatureCollection", "features": [{"id": "10.1016/j.eja.2022.126569", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:16:30Z", "type": "Journal Article", "created": "2022-07-08", "title": "Mixing process-based and data-driven approaches in yield prediction", "description": "Yield prediction models can be divided between data-driven and process-based models (crop growth models). The first category contains many different types of models with parameters learned from the data themselves and where domain knowledge is only used to select the predictors and engineer features. In the second category, models are based upon biophysical principles, whose structure and parameters are derived primarily from domain knowledge. Here we investigate if the integration of the two approaches can be beneficial as it allows to overcome the limitations of the two approaches taken individually - lack of sufficiently large, reliable and orthogonal datasets for data-driven approaches and the need of many inputs for process-based models. The applications of the two categories of models have been reviewed, paying special attention to the cases where the two approaches have been mixed. By analysing the literature we identified three major cases of integration between the two approaches: (1) using crop growth models to engineer features and expand the predictors space, (2) use data-driven approaches to estimate missing inputs for process-based models (3) using data-driven approaches to produce meta-models to reduce computation burden. Finally we propose a methodology based on metamodels and transfer learning to integrate data-driven and process-based approaches.", "keywords": ["Process-based", "0106 biological sciences", "2. Zero hunger", "Artificial intelligence", "Crop growth models", "04 agricultural and veterinary sciences", "Data-driven", "01 natural sciences", "Yield prediction", "Dynamic crop growth models", "Surrogate models", "0401 agriculture", " forestry", " and fisheries", "Crop models", "Metamodels", "Neural networks"]}, "links": [{"href": "https://doi.org/10.1016/j.eja.2022.126569"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/European%20Journal%20of%20Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.eja.2022.126569", "name": "item", "description": "10.1016/j.eja.2022.126569", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.eja.2022.126569"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-09-01T00:00:00Z"}}, {"id": "10.1016/j.scitotenv.2021.152880", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:17:21Z", "type": "Journal Article", "created": "2022-01-06", "title": "Exploring the potential role of environmental and multi-source satellite data in crop yield prediction across Northeast China", "description": "Open AccessLe d\u00e9veloppement d'un syst\u00e8me pr\u00e9cis de pr\u00e9diction du rendement des cultures \u00e0 grande \u00e9chelle est d'une importance primordiale pour la gestion des ressources agricoles et la s\u00e9curit\u00e9 alimentaire mondiale. L'observation de la Terre fournit une source unique d'informations pour surveiller les cultures \u00e0 partir d'une diversit\u00e9 de gammes spectrales. Cependant, l'utilisation int\u00e9gr\u00e9e de ces donn\u00e9es et de leurs valeurs dans la pr\u00e9diction du rendement des cultures est encore peu \u00e9tudi\u00e9e. Ici, nous avons propos\u00e9 la combinaison de donn\u00e9es environnementales (climat, sol, g\u00e9ographie et topographie) avec de multiples donn\u00e9es satellitaires (indices de v\u00e9g\u00e9tation optiques, fluorescence induite par le soleil (SIF), temp\u00e9rature de surface du sol (LST) et profondeur optique de la v\u00e9g\u00e9tation micro-ondes (VOD)) dans le cadre pour estimer le rendement des cultures de ma\u00efs, de riz et de soja dans le nord-est de la Chine, et leur valeur unique et leur influence relative sur la pr\u00e9diction du rendement ont \u00e9t\u00e9 \u00e9valu\u00e9es. Deux m\u00e9thodes de r\u00e9gression lin\u00e9aire, trois m\u00e9thodes d'apprentissage automatique (ML) et un mod\u00e8le d'ensemble ML ont \u00e9t\u00e9 adopt\u00e9s pour construire des mod\u00e8les de pr\u00e9diction de rendement. Les r\u00e9sultats ont montr\u00e9 que les m\u00e9thodes individuelles de ML surpassaient les m\u00e9thodes de r\u00e9gression lin\u00e9aire, le mod\u00e8le d'ensemble de ML a encore am\u00e9lior\u00e9 les mod\u00e8les de ML uniques. De plus, les mod\u00e8les avec plus d'intrants ont obtenu de meilleures performances, la combinaison de donn\u00e9es satellitaires avec des donn\u00e9es environnementales, qui expliquaient respectivement 72\u00a0%, 69\u00a0% et 57\u00a0% de la variabilit\u00e9 du rendement du ma\u00efs, du riz et du soja, a d\u00e9montr\u00e9 des performances de pr\u00e9diction du rendement sup\u00e9rieures \u00e0 celles des intrants individuels. Alors que les donn\u00e9es satellitaires ont contribu\u00e9 \u00e0 la pr\u00e9diction du rendement des cultures principalement au d\u00e9but de la pointe de la saison de croissance, les donn\u00e9es climatiques ont fourni des informations suppl\u00e9mentaires principalement \u00e0 la pointe de la fin de la saison. Nous avons \u00e9galement constat\u00e9 que l'utilisation combin\u00e9e de l'IVE, du LST et du SIF a am\u00e9lior\u00e9 la pr\u00e9cision du mod\u00e8le par rapport au mod\u00e8le d'IVE de r\u00e9f\u00e9rence. Cependant, les indices de v\u00e9g\u00e9tation bas\u00e9s sur l'optique partageaient des informations similaires et ne fournissaient pas beaucoup d'informations suppl\u00e9mentaires au-del\u00e0 de l'IVE. Les pr\u00e9visions de rendement en cours de saison ont montr\u00e9 que les rendements des cultures peuvent \u00eatre pr\u00e9vus de mani\u00e8re satisfaisante deux \u00e0 trois mois avant la r\u00e9colte. La g\u00e9ographie, la topographie, la VOD, l'IVE, les param\u00e8tres hydrauliques du sol et les param\u00e8tres nutritifs sont plus importants pour la pr\u00e9diction du rendement des cultures.", "keywords": ["Atmospheric sciences", "Climate", "Multi-source satellite data", "Normalized Difference Vegetation Index", "Engineering", "Pathology", "Climate change", "Urban Heat Islands and Mitigation Strategies", "Linear regression", "2. Zero hunger", "Global and Planetary Change", "Vegetation Monitoring", "Ecology", "Geography", "Statistics", "Agriculture", "Geology", "Remote Sensing in Vegetation Monitoring and Phenology", "04 agricultural and veterinary sciences", "Remote sensing", "Aerospace engineering", "Archaeology", "Physical Sciences", "Metallurgy", "Medicine", "Seasons", "Global Vegetation Models", "Biomass Estimation", "Regression analysis", "Vegetation (pathology)", "Crops", " Agricultural", "Environmental Engineering", "Environmental data", "Yield (engineering)", "Zea mays", "Environmental science", "Machine learning", "FOS: Mathematics", "Crop yield", "Biology", "Global Forest Drought Response and Climate Change", "FOS: Environmental engineering", "Predictive modelling", "Food security", "FOS: Earth and related environmental sciences", "15. Life on land", "Agronomy", "Materials science", "Yield prediction", "Satellite", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Growing season", "0401 agriculture", " forestry", " and fisheries", "Mathematics"], "contacts": [{"organization": "Zhenwang Li, Lei Ding, Donghui Xu,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.scitotenv.2021.152880"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.scitotenv.2021.152880", "name": "item", "description": "10.1016/j.scitotenv.2021.152880", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.scitotenv.2021.152880"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-01T00:00:00Z"}}, {"id": "10.1371/journal.pone.0184198", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:20:18Z", "type": "Journal Article", "created": "2017-09-01", "title": "Portfolio optimization for seed selection in diverse weather scenarios", "description": "The aim of this work was to develop a method for selection of optimal soybean varieties for the American Midwest using data analytics. We extracted the knowledge about 174 varieties from the dataset, which contained information about weather, soil, yield and regional statistical parameters. Next, we predicted the yield of each variety in each of 6,490 observed subregions of the Midwest. Furthermore, yield was predicted for all the possible weather scenarios approximated by 15 historical weather instances contained in the dataset. Using predicted yields and covariance between varieties through different weather scenarios, we performed portfolio optimisation. In this way, for each subregion, we obtained a selection of varieties, that proved superior to others in terms of the amount and stability of yield. According to the rules of Syngenta Crop Challenge, for which this research was conducted, we aggregated the results across all subregions and selected up to five soybean varieties that should be distributed across the network of seed retailers. The work presented in this paper was the winning solution for Syngenta Crop Challenge 2017.", "keywords": ["Crops", " Agricultural", "2. Zero hunger", "Models", " Statistical", "Glycine max", "Science", "Climate Change", "Q", "R", "Uncertainty", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "Portfolio optimisation", "Yield prediction", "Midwestern United States", "03 medical and health sciences", "0302 clinical medicine", "Seeds", "Medicine", "Regression Analysis", "0401 agriculture", " forestry", " and fisheries", "data analytics", "Weather", "Research Article"]}, "links": [{"href": "https://doi.org/10.1371/journal.pone.0184198"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PLOS%20ONE", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1371/journal.pone.0184198", "name": "item", "description": "10.1371/journal.pone.0184198", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1371/journal.pone.0184198"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-09-01T00:00:00Z"}}, {"id": "10.5281/zenodo.3997845", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:24:27Z", "type": "Journal Article", "title": "Predicting crop yield using data fusion by matrix factorization algorithm", "description": "How to choose the best hybrid of particular crop for the given location when there are thousands of choices of different varieties on the market? Yield is one of the best indicators for making the decision which seed varieties would be suitable. In order to choose the best hybrid for the given location we need to be able to predict crop yield of all existing hybrids for that location. Not all varieties will be suitable for all fields. This task may be seen as recommendation system where we want to recommend the best hybrid, the one that will give the highest yield, on the chosen farm. Predicting yield is a hard task. There are many parameters like weather, soil and genetics that influence on yield. The biggest challenge in improving the accuracy of prediction is to jointly analyze the complex interaction of all those parameters. In this task we used Data Fusion by Matrix Factorization (DFMF) algorithm that allows us to inference that complex interactions. DFMF uses a penalized matrix tri-factorization model that collectively tri-factorizes many data matrices such that each data matrix is decomposed into a product of tree latent matrices. Data that was analyzed in the paper comes from Syngenta Crop Challenge. It contains information about soil, weather and performance of various hybrids. We created matrix where the rows were hybrids and the columns were fields present in the chosen year and the entries of the matrix represent yield. Only ~10% of the matrix was known and the task was to complete the rest of the matrix, to find out the yield of all hybrid on all locations. In order to do that other data sources should help us. We wanted to enrich historical dataset as it is impossible to plant every seed variety on all fields. Getting new, enriched dataset would help us in making predictions for the next season, identifying the behavior of hybrids in different settings, deciding weather hybrid is tolerant or not to stresses...", "keywords": ["2. Zero hunger", "crop yield prediction", " data fusion", " matrix factorization", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3997845"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/EFITA", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3997845", "name": "item", "description": "10.5281/zenodo.3997845", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3997845"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.3997846", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:24:27Z", "type": "Journal Article", "title": "Predicting crop yield using data fusion by matrix factorization algorithm", "description": "How to choose the best hybrid of particular crop for the given location when there are thousands of choices of different varieties on the market? Yield is one of the best indicators for making the decision which seed varieties would be suitable. In order to choose the best hybrid for the given location we need to be able to predict crop yield of all existing hybrids for that location. Not all varieties will be suitable for all fields. This task may be seen as recommendation system where we want to recommend the best hybrid, the one that will give the highest yield, on the chosen farm. Predicting yield is a hard task. There are many parameters like weather, soil and genetics that influence on yield. The biggest challenge in improving the accuracy of prediction is to jointly analyze the complex interaction of all those parameters. In this task we used Data Fusion by Matrix Factorization (DFMF) algorithm that allows us to inference that complex interactions. DFMF uses a penalized matrix tri-factorization model that collectively tri-factorizes many data matrices such that each data matrix is decomposed into a product of tree latent matrices. Data that was analyzed in the paper comes from Syngenta Crop Challenge. It contains information about soil, weather and performance of various hybrids. We created matrix where the rows were hybrids and the columns were fields present in the chosen year and the entries of the matrix represent yield. Only ~10% of the matrix was known and the task was to complete the rest of the matrix, to find out the yield of all hybrid on all locations. In order to do that other data sources should help us. We wanted to enrich historical dataset as it is impossible to plant every seed variety on all fields. Getting new, enriched dataset would help us in making predictions for the next season, identifying the behavior of hybrids in different settings, deciding weather hybrid is tolerant or not to stresses...", "keywords": ["2. Zero hunger", "crop yield prediction", " data fusion", " matrix factorization", "15. Life on land"], "contacts": [{"organization": "Brki\u0107, Milica, Brdar, Sanja, Crnojevi\u0107, Vladimir,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3997846"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/EFITA", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3997846", "name": "item", "description": "10.5281/zenodo.3997846", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3997846"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.7067428", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:24:41Z", "type": "Report", "title": "Combining data and models for decisions in precision agriculture", "description": "In the face of a growing world population, declining land reserves and climate change, there is an urgent need to increase the efficiency of the production of food. Precision agriculture (PA) is one of the pathways in which the efficiency of agriculture can be increased. The profitability and sustainability of production of potatoes in The Netherlands can be increased by at least 20% by employing a range of PA technologies that are currently available to commercial growers. The systematic collection and processing of data is the cornerstone of precision agriculture. On the one hand, these data are used to derive models that describe the effect of weather, soil, and management on crop growth. On the other hand, data and models are used to support decision-making about when and where to apply inputs: fertilizers, crop protection agents, and irrigation water.", "keywords": ["2. Zero hunger", "13. Climate action", "precision agriculture ", " yield prediction", "15. Life on land"], "contacts": [{"organization": "Van Evert, Frits, Frenk Jan Baron, Been, Thomas, Berghuijs, Herman, Brdar, Sanja, Idse Hoving, Kessel, Geert, Mimi\u0107, Gordan, Van Randen, Yke, Riemens, Marleen, Kempenaar, Corn\u00e9,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7067428"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7067428", "name": "item", "description": "10.5281/zenodo.7067428", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7067428"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-05-16T00:00:00Z"}}, {"id": "10.5281/zenodo.7067429", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:24:41Z", "type": "Report", "title": "Combining data and models for decisions in precision agriculture", "description": "In the face of a growing world population, declining land reserves and climate change, there is an urgent need to increase the efficiency of the production of food. Precision agriculture (PA) is one of the pathways in which the efficiency of agriculture can be increased. The profitability and sustainability of production of potatoes in The Netherlands can be increased by at least 20% by employing a range of PA technologies that are currently available to commercial growers. The systematic collection and processing of data is the cornerstone of precision agriculture. On the one hand, these data are used to derive models that describe the effect of weather, soil, and management on crop growth. On the other hand, data and models are used to support decision-making about when and where to apply inputs: fertilizers, crop protection agents, and irrigation water.", "keywords": ["2. Zero hunger", "13. Climate action", "precision agriculture ", " yield prediction", "15. Life on land"], "contacts": [{"organization": "Van Evert, Frits, Frenk Jan Baron, Been, Thomas, Berghuijs, Herman, Brdar, Sanja, Idse Hoving, Kessel, Geert, Mimi\u0107, Gordan, Van Randen, Yke, Riemens, Marleen, Kempenaar, Corn\u00e9,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7067429"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7067429", "name": "item", "description": "10.5281/zenodo.7067429", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7067429"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-05-16T00:00:00Z"}}, {"id": "2753196607", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-30T16:27:04Z", "type": "Journal Article", "created": "2017-09-01", "title": "Portfolio optimization for seed selection in diverse weather scenarios", "description": "The aim of this work was to develop a method for selection of optimal soybean varieties for the American Midwest using data analytics. We extracted the knowledge about 174 varieties from the dataset, which contained information about weather, soil, yield and regional statistical parameters. Next, we predicted the yield of each variety in each of 6,490 observed subregions of the Midwest. Furthermore, yield was predicted for all the possible weather scenarios approximated by 15 historical weather instances contained in the dataset. Using predicted yields and covariance between varieties through different weather scenarios, we performed portfolio optimisation. In this way, for each subregion, we obtained a selection of varieties, that proved superior to others in terms of the amount and stability of yield. According to the rules of Syngenta Crop Challenge, for which this research was conducted, we aggregated the results across all subregions and selected up to five soybean varieties that should be distributed across the network of seed retailers. The work presented in this paper was the winning solution for Syngenta Crop Challenge 2017.", "keywords": ["Crops", " Agricultural", "2. Zero hunger", "Models", " Statistical", "Glycine max", "Science", "Climate Change", "Q", "R", "Uncertainty", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "Portfolio optimisation", "Yield prediction", "Midwestern United States", "03 medical and health sciences", "0302 clinical medicine", "Seeds", "Medicine", "Regression Analysis", "0401 agriculture", " forestry", " and fisheries", "data analytics", "Weather", "Research Article"]}, "links": [{"href": "https://doi.org/2753196607"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PLOS%20ONE", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2753196607", "name": "item", "description": "2753196607", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2753196607"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-09-01T00:00:00Z"}}, {"id": "34998760", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:27:45Z", "type": "Journal Article", "created": "2022-01-06", "title": "Exploring the potential role of environmental and multi-source satellite data in crop yield prediction across Northeast China", "description": "Open AccessLe d\u00e9veloppement d'un syst\u00e8me pr\u00e9cis de pr\u00e9diction du rendement des cultures \u00e0 grande \u00e9chelle est d'une importance primordiale pour la gestion des ressources agricoles et la s\u00e9curit\u00e9 alimentaire mondiale. L'observation de la Terre fournit une source unique d'informations pour surveiller les cultures \u00e0 partir d'une diversit\u00e9 de gammes spectrales. Cependant, l'utilisation int\u00e9gr\u00e9e de ces donn\u00e9es et de leurs valeurs dans la pr\u00e9diction du rendement des cultures est encore peu \u00e9tudi\u00e9e. Ici, nous avons propos\u00e9 la combinaison de donn\u00e9es environnementales (climat, sol, g\u00e9ographie et topographie) avec de multiples donn\u00e9es satellitaires (indices de v\u00e9g\u00e9tation optiques, fluorescence induite par le soleil (SIF), temp\u00e9rature de surface du sol (LST) et profondeur optique de la v\u00e9g\u00e9tation micro-ondes (VOD)) dans le cadre pour estimer le rendement des cultures de ma\u00efs, de riz et de soja dans le nord-est de la Chine, et leur valeur unique et leur influence relative sur la pr\u00e9diction du rendement ont \u00e9t\u00e9 \u00e9valu\u00e9es. Deux m\u00e9thodes de r\u00e9gression lin\u00e9aire, trois m\u00e9thodes d'apprentissage automatique (ML) et un mod\u00e8le d'ensemble ML ont \u00e9t\u00e9 adopt\u00e9s pour construire des mod\u00e8les de pr\u00e9diction de rendement. Les r\u00e9sultats ont montr\u00e9 que les m\u00e9thodes individuelles de ML surpassaient les m\u00e9thodes de r\u00e9gression lin\u00e9aire, le mod\u00e8le d'ensemble de ML a encore am\u00e9lior\u00e9 les mod\u00e8les de ML uniques. De plus, les mod\u00e8les avec plus d'intrants ont obtenu de meilleures performances, la combinaison de donn\u00e9es satellitaires avec des donn\u00e9es environnementales, qui expliquaient respectivement 72\u00a0%, 69\u00a0% et 57\u00a0% de la variabilit\u00e9 du rendement du ma\u00efs, du riz et du soja, a d\u00e9montr\u00e9 des performances de pr\u00e9diction du rendement sup\u00e9rieures \u00e0 celles des intrants individuels. Alors que les donn\u00e9es satellitaires ont contribu\u00e9 \u00e0 la pr\u00e9diction du rendement des cultures principalement au d\u00e9but de la pointe de la saison de croissance, les donn\u00e9es climatiques ont fourni des informations suppl\u00e9mentaires principalement \u00e0 la pointe de la fin de la saison. Nous avons \u00e9galement constat\u00e9 que l'utilisation combin\u00e9e de l'IVE, du LST et du SIF a am\u00e9lior\u00e9 la pr\u00e9cision du mod\u00e8le par rapport au mod\u00e8le d'IVE de r\u00e9f\u00e9rence. Cependant, les indices de v\u00e9g\u00e9tation bas\u00e9s sur l'optique partageaient des informations similaires et ne fournissaient pas beaucoup d'informations suppl\u00e9mentaires au-del\u00e0 de l'IVE. Les pr\u00e9visions de rendement en cours de saison ont montr\u00e9 que les rendements des cultures peuvent \u00eatre pr\u00e9vus de mani\u00e8re satisfaisante deux \u00e0 trois mois avant la r\u00e9colte. La g\u00e9ographie, la topographie, la VOD, l'IVE, les param\u00e8tres hydrauliques du sol et les param\u00e8tres nutritifs sont plus importants pour la pr\u00e9diction du rendement des cultures.", "keywords": ["Atmospheric sciences", "Climate", "Multi-source satellite data", "Normalized Difference Vegetation Index", "Engineering", "Pathology", "Climate change", "Urban Heat Islands and Mitigation Strategies", "Linear regression", "2. Zero hunger", "Global and Planetary Change", "Vegetation Monitoring", "Ecology", "Geography", "Statistics", "Agriculture", "Geology", "Remote Sensing in Vegetation Monitoring and Phenology", "04 agricultural and veterinary sciences", "Remote sensing", "Aerospace engineering", "Archaeology", "Physical Sciences", "Metallurgy", "Medicine", "Seasons", "Global Vegetation Models", "Biomass Estimation", "Regression analysis", "Vegetation (pathology)", "Crops", " Agricultural", "Environmental Engineering", "Environmental data", "Yield (engineering)", "Zea mays", "Environmental science", "Machine learning", "FOS: Mathematics", "Crop yield", "Biology", "Global Forest Drought Response and Climate Change", "FOS: Environmental engineering", "Predictive modelling", "Food security", "FOS: Earth and related environmental sciences", "15. Life on land", "Agronomy", "Materials science", "Yield prediction", "Satellite", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Growing season", "0401 agriculture", " forestry", " and fisheries", "Mathematics"], "contacts": [{"organization": "Zhenwang Li, Lei Ding, Donghui Xu,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/34998760"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "34998760", "name": "item", "description": "34998760", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/34998760"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Yield+prediction&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Yield+prediction&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Yield+prediction&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Yield+prediction&offset=9", "hreflang": "en-US"}], "numberMatched": 9, "numberReturned": 9, "distributedFeatures": [], "timeStamp": "2026-05-30T19:10:46.198491Z"}