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Bibliothèque Evaluation and Screening of Co-Culture Farming Models in Rice Field Based on Food Productivity

Evaluation and Screening of Co-Culture Farming Models in Rice Field Based on Food Productivity

Evaluation and Screening of Co-Culture Farming Models in Rice Field Based on Food Productivity

Resource information

Date of publication
Décembre 2019
Resource Language
ISBN / Resource ID
LP-midp002343

Traditional farming practice of rice field co-culture is a time-tested example of sustainable agriculture, which increases food productivity of arable land with few adverse environmental impacts. However, the small-scale farming practice needs to be adjusted for modern agricultural production. Screening of rice field co-culture farming models is important in deciding the suitable model for industry-wide promotion. In this study, we aim to find the optimal rice field co-culture farming models for large-scale application, based on the notion of food productivity. We used experimental data from the Jiangsu Province of China and applied food-equivalent unit and arable-land-equivalent unit methods to examine applicable protocols for large-scale promotion of rice field co-culture farming models. Results indicate that the rice-loach and rice-catfish models achieve the highest food productivity; the rice-duck model increases the rice yield, while the rice-turtle and rice-crayfish models generate extra economic profits. Simultaneously considering economic benefits, staple food security, and regional food output, we recommend the rice-duck, rice-crayfish, and rice-catfish models. Simulating provincial promotion of the above three models, we conclude that food output increases from all three recommended models, as well as the land production capacity. The rice-catfish co-culture model has the most substantial food productivity. None of the three models threatens staple food security, as they do not compete for land resources with rice cultivation.

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Authors and Publishers

Author(s), editor(s), contributor(s)

Jin, TaoGe, CandiGao, HuiZhang, HongchengSun, Xiaolong

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Geographical focus