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Determinants of agricultural diversification in Brazil: a spatial econometric analysis

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Abstract

This research has as main objective to analyze the behavior and the importance of agricultural diversification for Brazil, considering its States, for the period from 2002 to 2018. We will propose an analytical model to make it possible to identify the determinants of agricultural diversification in Brazil. Empirically, the study estimates an SLX model using panel data and considers the spillover effects, highlighting the importance of location and neighborhood. The study's findings indicate a continued decline in crop diversity with a strong tendency to productive specialization in Brazilian agriculture, mainly in the states located in the Midwest and South regions of the country. The average growth rates of the indices presented negative values for the analysis period: − 0.41% per year for the Simpson index, − 0.58% per year for the Shannon index, and − 0.91% per year for the effective number of crops. It is important to note that some states are allocating practically the entire agricultural area to three or four dominant crops. As for the determinants of agricultural diversification, the results for Brazil are in line with the specialized literature.

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Fig. 1

Source: Research results. Obs.—Left axis referring to Shannon and the right referring to Simpson. Medium values

Fig. 2

Source: Research data

Fig. 3

Source: Research data

Fig. 4

Source: Research data

Fig. 5

Source: Research data

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Availability of data and material

The datasets used in the research are public and are cited in the reference list.

Notes

  1. According to Magurran (1988) this initial version of the index is given by \(\sum_{i=1}^{n}{p}_{i}^{2}\).

  2. Respectively, Spatial Error Model (SEM), Spatial Autoregressive Model (SAR), Spatial Autoregressive Combined model (SAC), and Spatial Durbin model (SDM).

  3. See subsection 4.3 Robustness check.

  4. We thank an anonymous referee to call attention to this point.

  5. The growth rates were calculated according to the following expression: \(\mathrm{ln}{Y}_{t}={\beta }_{0}+{\beta }_{1}t.\)

  6. We thank an anonymous referee for their suggestions on this point.

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Funding

This study was supported in part by Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq, Brasil (Post Doctoral Scholarship for author JLP - Grant Number: 104118/2020-2). Author ALSC thanks financial support of Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) – Grant Number 88881.172635/2018-01.

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Authors and Affiliations

  1. Department of Economics, State University of Maringá, Av. Colombo, 5790, Maringá, PR, 87020-900, Brazil

    José Luiz Parré

  2. Department of Economics, University of São Paulo, Av. Prof. Luciano Gualberto, 908, FEA I - Cidade Universitaria, São Paulo, SP, Brazil

    André Luis Squarize Chagas

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  1. José Luiz Parré
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Contributions

José Luiz Parré: Conceptualization, Methodology, Software, Formal analysis, Data Curation, Writing—Original draft. André Luis Squarize Chagas: Conceptualization, Methodology, Writing-Reviewing, Supervision.

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Correspondence to José Luiz Parré.

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Appendix

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Table 7 ADF unit root test
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7,

Table 8 Phillips–Perron unit root test
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8 and

Table 9 Maddala–Wu unit root test
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9.

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Parré, J.L., Chagas, A.L.S. Determinants of agricultural diversification in Brazil: a spatial econometric analysis. Lett Spat Resour Sci 15, 173–195 (2022). https://doi.org/10.1007/s12076-021-00295-0

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