Agrometeorological models to forecast açaí (Euterpe oleracea Mart.) yield in the Eastern Amazon

Machine learning algorithms for soybean yield forecasting in the Brazilian Cerrado

BACKGROUND

We evaluated different machine learning (ML) models for predicting soybean productivity up to 1 month in advance for the Matopiba agricultural frontier (States of Maranhão, Tocantins, Piauí, and Bahia). We collected meteorological data on the NASA-POWER platform and soybean yield on the SIDRA/IBGE base between 2008 and 2017. The ML models evaluated were random forest (RF), artificial neural networks, radial base support vector machines (SVM_RBF), linear model and polynomial regression. To assess the performance of the models, cross-validation was used, obtaining the value of precision by R2 , accuracy by root mean square error (RMSE), and trend by the mean error of the estimate (EME).

RESULTS

The results showed that the RF algorithm achieves the highest precision and accuracy, with R2 of 0.81, RMSE of 176.93 kg ha-1 and trend (EME) of 1.99 kg ha-1 . On the other hand, the SVM_RBF algorithm showed the lowest performance, with R2 of 0.74, RMSE of 213.58 kg ha-1 and EME of -15.06 kg ha-1 . The average yield values predicted by the models were within the expected range for the region, which has a historical average value of 2.730 kg ha-1 .

CONCLUSION

All models had acceptable precision, accuracy and trend indices, which makes it possible to use all algorithms to be applied in the prediction of soybean crop yield, observing the particularities of the region to be studied, in addition to being a useful tool for agricultural planning and decision making in soy-producing regions such as the Brazilian Cerrado. © 2021 Society of Chemical Industry.

Oil Palm Yield Estimation Based on Vegetation and Humidity Indices Generated from Satellite Images and Machine Learning Techniques

Palm oil has become one of the most consumed vegetable oils in the world, and it is a key element in profitable global value chains. In Costa Rica, oil palm cultivation is one of the three crops with the largest occupied agricultural area. The objective of this study was to explain and predict yield in safe time lags for production management by using free-access satellite images. To this end, machine learning methods were performed to a 20-year data set of an oil palm plantation located in the Central Pacific Region of Costa Rica and the corresponding vegetation indices obtained from LANDSAT satellite images. Since the best correlations corresponded to a one-year time lag, the predictive models Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), Recursive Partitioning and Regression Trees (RPART), and Neural Network (NN) were built for a Time-lag 1. These models were applied to all genetic material and to the predominant variety (AVROS) separately. While NN showed the best performance for multispecies information (r2 = 0.8139, NSE = 0.8131, RMSE = 0.3437, MAE = 0.2605), RF showed a better fit for AVROS (r2 = 0.8214, NSE = 0.8020, RMSE = 0.3452, MAE = 0.2669). The most relevant vegetation indices (NDMI, MSI) are related to water in the plant. The study also determined that data distribution must be considered for the prediction and evaluation of the oil palm yield in the area under study. The estimation methods of this study provide information on the identification of important variables (NDMI) to characterize palm oil yield. Additionally, it generates a scenario with acceptable uncertainties on the yield forecast one year in advance. This information is of direct interest to the oil palm industry.

Non-Timber Forest Products in Brazil: A Bibliometric and a State of the Art Review

Non-timber forest products (NTFPs) are a consolidated source of income and acquisition of inputs from forest environments. Therefore, the objective of this work was to carry out a collection of publications on NTFPs in Brazil, until 2019, available in the Scopus database, presenting a bibliometric review and the state of the art of this theme from the evaluation of these publications, discussing the challenges of Brazilian legislation on NTFPs. After screening the articles of interest, 196 documents were evaluated, in which they were observed institutions and authors, analyzing networks of citations and terms used, areas of forest sciences and sciences that encompass the most explored biomes and the most studied species. The results showed that the concern to research on NTFPs in Brazil began in the 1990s, with an increase in the number of publications over the years. Besides that, the research on NTFPs is multidisciplinary, with emphasis on the areas of Agricultural and Biological Sciences and Environmental Science. For better regulation of the process of exploration and management of NTFPs in Brazil, the need to create specific legislation that takes into account factors such as the phytogeographic domain the explored area, producing species, and the products and co-products obtained was observed.