Sustainable Nitrogen Management in Rice Farming: Spatial Patterns of Nitrogen Availability and Implications for Community-Level Practices

Nobuhito Sekiya (), Ayaka Mae, Mchuno Alfred Peter, Beno Kiwale Anton, Tasuku Eigen, Saki Yamayoshi, Masaru Sakai, Kunio Watanabe and Takaharu Kameoka
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Nobuhito Sekiya: Graduate School of Bioresources, Mie University, Tsu 514-8507, Mie, Japan
Ayaka Mae: Graduate School of Bioresources, Mie University, Tsu 514-8507, Mie, Japan
Mchuno Alfred Peter: Graduate School of Bioresources, Mie University, Tsu 514-8507, Mie, Japan
Beno Kiwale Anton: Graduate School of Bioresources, Mie University, Tsu 514-8507, Mie, Japan
Tasuku Eigen: Graduate School of Bioresources, Mie University, Tsu 514-8507, Mie, Japan
Saki Yamayoshi: Graduate School of Bioresources, Mie University, Tsu 514-8507, Mie, Japan
Masaru Sakai: Graduate School of Bioresources, Mie University, Tsu 514-8507, Mie, Japan
Kunio Watanabe: Graduate School of Bioresources, Mie University, Tsu 514-8507, Mie, Japan
Takaharu Kameoka: Graduate School of Bioresources, Mie University, Tsu 514-8507, Mie, Japan

Sustainability, 2024, vol. 16, issue 22, 1-13

Abstract: Sustainable nitrogen management is crucial for long-term food security and environmental protection in rice farming systems. However, the spatial patterns of nitrogen availability at the community level remain poorly understood, hindering the development of effective sustainable management strategies. This study introduces a novel application of spatial autoregressive analysis to investigate available nitrogen distribution in paddy soils across a rice farming community in Kyoto, Japan. Soil samples from 61 plots, including organically farmed ones, were analyzed for available nitrogen and various physicochemical properties. Contrary to the hypothesis of high variability between adjacent plots, significant positive spatial autocorrelation in available nitrogen was observed, revealing previously unrecognized community-level patterns. The spatial Durbin model outperformed traditional regression approaches and revealed complex spatial interactions in soil properties. Water-soluble organic carbon and humus content showed strong but opposing effects, with a positive direct impact but negative spatial interaction, suggesting topography-driven accumulation processes. Water-soluble nitrogen exhibited reverse patterns with negative direct effects but positive spatial interaction, indicating potential nutrient transport through water movement. These findings highlight the importance of considering both direct and indirect spatial effects in understanding soil fertility patterns, challenging the conventional plot-by-plot management approach. This methodological advancement provides new perspectives for more effective, community-scale soil management strategies in rice farming systems. Moreover, it demonstrates an innovative approach to maximizing the value of outsourced soil analysis data, providing a model for more comprehensive utilization of such data in agricultural research. By enabling more targeted and efficient nitrogen management practices that consider both plot-level processes and landscape-scale interactions, this study potentially contributes to the development of more sustainable and resilient rice production systems.

Keywords: community-level distribution; nitrogen availability; spatial autocorrelation; spatial autoregression; soil fertility pattern (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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