Modelling wheat yield with antecedent information, satellite and climate data using machine learning methods in Mexico

Gómez, Diego; Salvador, Pablo; Sanz, Julia; Casanova, José

doi:10.1016/j.agrformet.2020.108317

Cited by 57 publications

(26 citation statements)

References 39 publications

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“…The other works carried out in the field of food security that makes use of ML focus only on one dimension defined by FAO (9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24), more specifically on the dimension of physical availability of food (primarily focused on predicting crop yield). So it would not give us a total vision of food security problems according to what the FAO proposes.…”

Section: Discussionmentioning

confidence: 99%

“…Machine Learning models (ML) can be potentially used for the calculation of food security. Nevertheless, current models are focused on analyzing the first FAO's dimension of physical availability of food through the prediction of crop yield (9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24), and are not used to calculate an index itself. On the other hand, other techniques such as GHI and FIES used for calculating a food security index at a regional level mainly focus on nutritional or economic and physical access aspects (25).…”

Section: Introductionmentioning

confidence: 99%

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Multi-Dimensional Dataset of Open Data and Satellite Images for Characterization of Food Security and Nutrition

et al. 2022

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BackgroundNutrition is one of the main factors affecting the development and quality of life of a person. From a public health perspective, food security is an essential social determinant for promoting healthy nutrition. Food security embraces four dimensions: physical availability of food, economic and physical access to food, food utilization, and the sustainability of the dimensions above. Integrally addressing the four dimensions is vital. Surprisingly most of the works focused on a single dimension of food security: the physical availability of food.ObjectiveThe paper proposes a multi-dimensional dataset of open data and satellite images to characterize food security in the department of Cauca, Colombia.MethodsThe food security dataset integrates multiple open data sources; therefore, the Cross-Industry Standard Process for Data Mining methodology was used to guide the construction of the dataset. It includes sources such as population and agricultural census, nutrition surveys, and satellite images.ResultsAn open multidimensional dataset for the Department of Cauca with 926 attributes and 9 rows (each row representing a Municipality) from multiple sources in Colombia, is configured. Then, machine learning models were used to characterize food security and nutrition in the Cauca Department. As a result, The Food security index calculated for Cauca using a linear regression model (Mean Absolute Error of 0.391) is 57.444 in a range between 0 and 100, with 100 the best score. Also, an approach for extracting four features (Agriculture, Habitation, Road, Water) of satellite images were tested with the ResNet50 model trained from scratch, having the best performance with a macro-accuracy, macro-precision, macro-recall, and macro-F1-score of 91.7, 86.2, 66.91, and 74.92%, respectively.ConclusionIt shows how the CRISP-DM methodology can be used to create an open public health data repository. Furthermore, this methodology could be generalized to other types of problems requiring the creation of a dataset. In addition, the use of satellite images presents an alternative for places where data collection is challenging. The model and methodology proposed based on open data become a low-cost and effective solution that could be used by decision-makers, especially in developing countries, to support food security planning.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Dimensional Dataset of Open Data and Satellite Images for Characterization of Food Security and Nutrition

et al. 2022

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“…Wheat ( Triticum aestivum ) is one of the most important cereal crops and vital staple food worldwide 1 , 2 , for the reasons that it grows in both the temperate and warmer regions due to its resilience to drought and frosts. Moreover, wheat grain is nutritious and composed of starch, fiber, vitamins B and E, iron and antioxidants.…”

Section: Introductionmentioning

confidence: 99%

Wheat (Triticum aestivum) adaptability evaluation in a semi-arid region of Central Morocco using APSIM model

Briak

Kebede

2021

Sci Rep

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In this study, we evaluated the suitability of semi-arid region of Central Morocco for wheat production using Agricultural Production Systems sIMulator (APSIM) considering weather, soil properties and crop management production factors. Model calibration was carried out using data collected from field trials. A quantitative statistics, i.e., root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and index of agreement (d) were used in model performance evaluation. Furthermore, series of simulations were performed to simulate the future scenarios of wheat productivity based on climate projection; the optimum sowing date under water deficit condition and selection of appropriate wheat varieties. The study showed that the performance of the model was fairly accurate as judged by having RMSE = 0.13, NSE = 0.95, and d = 0.98. The realization of future climate data projection and their integration into the APSIM model allowed us to obtain future scenarios of wheat yield that vary between 0 and 2.33 t/ha throughout the study period. The simulated result confirmed that the yield obtained from plots seeded between 25 October and 25 November was higher than that of sown until 05 January. From the several varieties tested, Hartog, Sunstate, Wollaroi, Batten and Sapphire were yielded comparatively higher than the locale variety Marzak. In conclusion, APSIM-Wheat model could be used as a promising tool to identify the best management practices such as determining the sowing date and selection of crop variety based on the length of the crop cycle for adapting and mitigating climate change.

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“…Kamir et al [ 7 ] compared several base learners including random forest, support vector machine, and k-nearest neighbor algorithm using climate records and satellite image time series to predict Australian wheat yield. In addition, Goméz et al [ 8 ] compared the performance of eight machine learning models to predict wheat yield at a municipal level in Mexico. Wolanin et al [ 9 ] applied a convolutional neural network, ridge regression, and random forest to predict wheat yield in the Indian Wheat Belt.…”

Section: Introductionmentioning

confidence: 99%

Prediction of municipality-level winter wheat yield based on meteorological data using machine learning in Hokkaido, Japan

et al. 2021

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This study analyzed meteorological constraints on winter wheat yield in the northern Japanese island, Hokkaido, and developed a machine learning model to predict municipality-level yields from meteorological data. Compared to most wheat producing areas, this island is characterized by wet climate owing to greater annual precipitation and abundant snowmelt water supply in spring. Based on yield statistics collected from 119 municipalities for 14 years (N = 1,516) and high-resolution surface meteorological data, correlation analyses showed that precipitation, daily minimum air temperature, and irradiance during the grain-filling period had significant effects on the yield throughout the island while the effect of snow depth in early winter and spring was dependent on sites. Using 10-d mean meteorological data within a certain period between seeding and harvest as predictor variables and one-year-leave-out cross-validation procedure, performance of machine learning models based on neural network (NN), random forest (RF), support vector machine regression (SVR), partial least squares regression (PLS), and cubist regression (CB) were compared to a multiple linear regression model (MLR) and a null model that returns an average yield of the municipality. The root mean square errors of PLS, SVR, and RF were 872, 982, and 1,024 kg ha−1 and were smaller than those of MLR (1,068 kg ha−1) and null model (1,035 kg ha−1). These models outperformed the controls in other metrics including Pearson’s correlation coefficient and Nash-Sutcliffe efficiency. Variable importance analysis on PLS indicated that minimum air temperature and precipitation during the grain-filling period had major roles in the prediction and excluding predictors in this period (i.e. yield forecast with a longer lead-time) decreased forecast performance of the models. These results were consistent with our understanding of meteorological impacts on wheat yield, suggesting usefulness of explainable machine learning in meteorological crop yield prediction under wet climate.

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Modelling wheat yield with antecedent information, satellite and climate data using machine learning methods in Mexico

Cited by 57 publications

References 39 publications

Multi-Dimensional Dataset of Open Data and Satellite Images for Characterization of Food Security and Nutrition

Multi-Dimensional Dataset of Open Data and Satellite Images for Characterization of Food Security and Nutrition

Wheat (Triticum aestivum) adaptability evaluation in a semi-arid region of Central Morocco using APSIM model

Prediction of municipality-level winter wheat yield based on meteorological data using machine learning in Hokkaido, Japan

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