System Dynamics Application as a Tool to Estimate Environmental Effects of Irrigation, Pest and Weed Control Considering a Traditional Sugarcane Crop and an Organic Sugarcane Crop

Maria Alejandra Coronado Racines, Juan Carlos Osorio Gómez, Dayhanna Stephania Vargas Mesa
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Abstract

The cultivation of sugar cane generates impacts on ecosystems, two of the four most relevant impacts are environmental, on the one hand, the generation of greater water stress due to irrigation needs, in addition to the impact on biodiversity and endemic species due to land use, highlighting the need for the development of policies for sugarcane production trying to mitigate these impacts [1]. This is how a dynamic simulation is proposed that addresses elements of sugarcane cultivation considering a case of traditional plantation and another organic one to estimate the environmental effects produced by irrigation, which generates depletion of the water resource, and the control of pests and weeds that cause contamination in the soil through the application of chemicals. The proposed model was applied using the Vensim PLE software considering interrelationships between these elements. In this way, it was possible to conclude that the depletion of the water resource and the contamination of the soil is lower in organic cultivation, consuming 10.5% less water, in addition they are not considered pollutants in the soil, but in the traditional cultivation reaches 1674 l/ha. It is important to note that for the model to be able to correctly support decision-making, additional aspects that affect the yield of each type of crop, as well as its profit margin and volatility, must be considered.
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以传统甘蔗作物和有机甘蔗作物为例,系统动力学应用于灌溉、病虫害和杂草控制的环境效应评估
甘蔗的种植会对生态系统产生影响,四个最相关的影响中有两个是环境影响,一方面,由于灌溉需求而产生更大的水资源压力,此外,由于土地利用对生物多样性和特有物种的影响,突出了制定甘蔗生产政策以减轻这些影响的必要性。这就是一个动态模拟是如何提出的,考虑到传统种植园和另一个有机种植园的情况下,解决甘蔗种植的要素,以估计灌溉产生的环境影响,这会导致水资源的枯竭,以及控制害虫和杂草,通过施用化学物质导致土壤污染。考虑到这些元素之间的相互关系,使用Vensim PLE软件应用所提出的模型。由此可以得出,有机栽培的水资源耗竭和土壤污染程度较低,耗水量少10.5%,此外它们不属于土壤污染物,但在传统栽培中达到1674 l/ha。值得注意的是,为了使模型能够正确地支持决策,必须考虑影响每种作物产量的其他方面,以及其利润率和波动性。
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