The NBCalCer model for calculating global benefits and costs of nitrogen fertilizer use for cereal cultivation: Model description, uncertainty analysis and validation
Alfredo Rodríguez , Hans J.M. van Grinsven , Rasmus Einarsson , Arthur H.W. Beusen , Alberto Sanz-Cobena , Luis Lassaletta
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引用次数: 0
Abstract
The challenge of achieving food security in a world with a growing population, given decreasing area of good arable land, and climate change impacts, necessitates structural system changes which includes a more efficient and less polluting agricultural practices. Cereals represent the major staple crop to feed the global population, and its production heavily relies on synthetic nitrogen (N) fertilizers. However, increasing fertilizer inputs for higher yields is not sustainable without proper management of soil, crop, inputs, water and nutrients. The NBCalCer tool was developed to evaluate options for a more sustainable cereal cultivation. NBCalCer quantifies long-term yield responses to N inputs and its implications for N budgets, N losses and environmental impacts for global countries. Crop benefits and environmental damages are monetized to assess benefit-cost consequences for the farming sector and society, to determine optimal N inputs for both sufficient grain supply and acceptable N pollution levels.
期刊介绍:
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.