A spatially based quantile regression forest model for mapping rural land values

Córdoba, Mariano; Carranza, Juan Pablo; Piumetto, Mario Andrés; Monzani, Federico; Balzarini, Mónica

doi:10.1016/j.jenvman.2021.112509

Cited by 22 publications

(10 citation statements)

References 30 publications

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“…Concerning interval prediction, it is reported that the performances of quantile regression algorithms often vary depending on the quantiles considered (Newcombe, 1998;Wang, 2008;David et al, 2018;He et al, 2020;Córdoba et al, 2021). This highlights the importance of evaluating these algorithms for a wide range of quantiles.…”

Section: Discussionmentioning

confidence: 99%

Performances of Machine Learning Algorithms in Predicting the Productivity of Conservation Agriculture at a Global Scale

Zhang

Gabrielle

et al. 2022

Front. Environ. Sci.

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Assessing the productive performance of conservation agriculture (CA) has become a major issue due to growing concerns about global food security and sustainability. Numerous experiments have been conducted to assess the performance of CA under various local conditions, and meta-analysis has become a standard approach in agricultural sector for analysing and summarizing the experimental data. Meta-analysis provides valuable synthetic information based on mean effect size estimation. However, summarizing large amounts of information by way of a single mean effect value is not always satisfactory, especially when considering agricultural practices. Indeed, their impacts on crop yields are often non-linear, and vary widely depending on a number of factors, including soil properties and local climate conditions. To address this issue, here we present a machine learning approach to produce data-driven global maps describing the spatial distribution of the productivity of CA versus conventional tillage (CT). Our objective is to evaluate and compare several machine-learning models for their ability in estimating the productivity of CA systems, and to analyse uncertainty in the model outputs. We consider different usages, including classification, point regression and quantile regression. Our approach covers the comparison of 12 different machine learning algorithms, model training, tuning with cross-validation, testing, and global projection of results. The performances of these algorithms are compared based on a recent global dataset including more than 4,000 pairs of crop yield data for CA vs. CT. We show that random forest has the best performance in classification and regression, while quantile regression forest performs better than quantile neural networks in quantile regression. The best algorithms are used to map crop productivity of CA vs. CT at the global scale, and results reveal that the performance of CA vs. CT is characterized by a strong spatial variability, and that the probability of yield gain with CA is highly dependent on geographical locations. This result demonstrates that our approach is much more informative than simply presenting average effect sizes produced by standard meta-analyses, and paves the way for such probabilistic, spatially-explicit approaches in many other fields of research.

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Section: Discussionmentioning

confidence: 99%

Performances of Machine Learning Algorithms in Predicting the Productivity of Conservation Agriculture at a Global Scale

Zhang

Gabrielle

et al. 2022

Front. Environ. Sci.

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“…In addition, spatial econometrics has recently been successfully combined with other econometric tools, resulting in models with a high degree of sophistication and complexity. Two main trends stand out in this process: (i) the incorporation of quantile methods that allow analysing the detailed contributions of different determinants via the conditional distribution of land prices (Kostov, 2009a;Lehn & Bahrs, 2018b;Sardaro et al, 2021), with the possibility of incorporating even machine learning elements into such models (Córdoba et al, 2021); and (ii) the combination of spatial econometrics with time series econometrics in studies that have focused on the evaluation of the spatiotemporal integration of land markets (Carmona & Rosés, 2012;Yang et al, 2017Yang et al, , 2019Grau et al, 2020).…”

Section: Theoretical Parameters: the Spatial Dimension Of Land Pricesmentioning

confidence: 99%

Spatial effects are determinants of agricultural land prices in Brazil

Marques

Telles

2023

Rev. Econ. Sociol. Rural

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This study aims to determine whether spatial effects are determinants of agricultural land prices in Brazil. For this purpose, data on the value of the bare land in municipalities in Brazil for 2020, provided by the Federal Revenue Service, were used. Although this database has national coverage, the contiguous data necessary for the application of spatial econometric models allowed us to cover the central-south region of the country. An exploratory spatial data analysis was performed, and the spatial Durbin econometric model was applied. Based on our results, there is a strong spatial dependence on land price, manifested both in the dependent variable, spatially lagged, and through the spillover effects of the independent variables. In addition, it was found that the degree of urbanization of a municipality, the municipal gross domestic product per capita, the average size of properties, the agricultural productivity, and the area dedicated to soybean planting in a rural property are also important determining factors in the pricing of agricultural land. Accordingly, it is possible to conclude that spatial effects influence the determination of agricultural land prices in Brazil.

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“…Além disso, recentemente, a econometria espacial também vem sendo combinada com sucesso com outras ferramentas econométricas, resultando em modelos de alto grau de sofisticação e complexidade. Duas tendências principais se destacam nesse processo: (i) a incorporação de métodos quantílicos, que permitem analisar a contribuição detalhada de diferentes determinantes ao longo da distribuição condicional dos preços da terra (Kostov, 2009a;Lehn & Bahrs, 2018b;Sardaro et al, 2021), com a possibilidade de incorporar, até mesmo, elementos de aprendizagem de máquinas aos modelos (Córdoba et al, 2021); e (ii) a combinação da econometria espacial com a econometria de séries temporais, cujos trabalhos têm se focado na avaliação da integração espaço-temporal dos mercados de terra (Carmona & Rosés, 2012;Yang et al, 2017Yang et al, , 2019Grau et al, 2020).…”

Section: Parâmetros Teóricos -A Dimensão Espacial Dos Preços Da Terraunclassified

Os efeitos espaciais são fatores determinantes dos preços das terras agrícolas no Brasil

Marques

Telles

2023

Rev. Econ. Sociol. Rural

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Resumo Este estudo tem como objetivo verificar se os efeitos espaciais são fatores determinantes dos preços das terras agrícolas no Brasil. Para isso, foram utilizados dados do valor da terra nua de municípios do Brasil, para o ano de 2020, disponibilizados pela Receita Federal. Embora a base de dados tenha cobertura nacional, os dados contíguos, necessários na aplicação dos modelos econométricos espaciais, permitiram abranger a região Centro-Sul do país. Foi realizada a análise exploratória de dados espaciais e aplicado o modelo econométrico Durbin espacial. Pelos resultados obtidos verifica-se a existência de forte dependência espacial no preço da terra, manifestada tanto na variável dependente, espacialmente defasada, quanto por meio de efeitos de transbordamento das variáveis independentes. Além disso, verificou-se que o grau de urbanização do município, o produto interno bruto per capita municipal, o tamanho médio das propriedades, a produtividade agrícola e a área dedicada ao plantio da soja, na propriedade rural, também são importantes fatores determinantes do preço das terras agrícolas. Conclui-se que efeitos espaciais têm influência na determinação dos preços das terras agrícolas no Brasil.

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A spatially based quantile regression forest model for mapping rural land values

Cited by 22 publications

References 30 publications

Performances of Machine Learning Algorithms in Predicting the Productivity of Conservation Agriculture at a Global Scale

Performances of Machine Learning Algorithms in Predicting the Productivity of Conservation Agriculture at a Global Scale

Spatial effects are determinants of agricultural land prices in Brazil

Os efeitos espaciais são fatores determinantes dos preços das terras agrícolas no Brasil

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