Document Type : Research Article
Authors
Ferdowsi university of Mashhad
Abstract
Introduction: Over the last two decades, awareness of resilience and sustainability and also efforts to reduce unsustainable production patterns have significantly increased. Hence, it is crucial to examine the resilience and sustainability of production systems. Resilience explains how well production systems withstand and/or rebound from aberration. Sustainability concept based on Commission’s words is: “development that meets the needs of the present without compromising the ability of future generations to meet their own needs”. The important issue relevant to resilience and sustainability and the resilience of farms/agricultural systems is, whether resilience or sustainability can be considered as a property of a system or needs to be understood as a process. Since both of them are not essentially opposed but have various theoretical and methodological implications, it is necessary to define a resilience and sustainability indicator. So, it is required to have an intelligent objective function for fairly balancing between production systems and dimensions of sustainable production to fulfill economic benefit and the resulting environmental benefit, etc. Based on the existing published literature, studies focusing on both resilience and sustainability indicators in industrial dairy farms by using multi-objective non-linear programming and swarm intelligence algorithm have not been carried out. Therefore, it is the aim of the present study to design the “automata resilience and sustainability indicator” for industrial dairy farms. The objective function has a hierarchical structure and in order to integrate these pillars into a single score, a value between zero and one, Analytic Hierarchy Process (AHP) has been used that the value of one means complete sustainability.
Material and Methods: The objective function should be maximized which has 5 main indicators including, environmental, economic, social, technological and political issues. Each indicator has some sub-indicators. So, we designed and modeled formulas for all of them. The value of objective function is normalized, therefore, its maximum possible value is "one", which indicates the complete resilience and sustainability of dairy farms. The resilience and sustainability indicator is obtained at three levels. Eight types of constraint sets are considered. Then, the model has been implemented using data of 30[1] industrial dairy farms in Khorasan-Razavi province of Iran during 2016.
Results and Discussion: The resilience and sustainability indicator across all farms was obtained 0.43 and which was low. One of the main reasons of unsustainability and inflexibility of dairy farms under study is the unsuitable use of resources and inputs. Therefore, the proposed model (Automata Resilience and Sustainability Indicator Model) was designed and optimized. Based on result the optimum resilience and sustainability achievable for the proposed dairy farm is 0.9598 (95.98%). Thus, the proposed model succeeds in determining the dairy farms' resilience and sustainability. Furthermore, it helps in setting up other operational parameters as determining the amount of cow manure produced, the man-working hours and labor expenditure. The obtained results should be further used as guidance for improving the resilience and sustainability of the manufacturing operation in dairy farms.
Conclusions: This study has introduced a formulation for a resilience and sustainability problem in process of production in the industrial dairy farm. The contribution of the proposed formulation is its ability to addresses all pillars of resilience and sustainability at the producing level. One of the main advantages of the proposed measure of resilience and sustainability is data collection that relies on data usually collected in all farms for revenue and cost analysis, cattle diet and quality control. This fact makes the model applicable to facilities introducing resilience and sustainability concepts. Thus contributes to promoting the implementation of sustainable practices in agricultural production, especially in developing countries, where still have a lack of resilience and sustainability awareness and related legislation. Using weight is important to the application of the objective function and also makes the model suitable for its intended usage in the dairy farms of developing countries. This model is applicable in the area of the optimum dairy cattle nutrition, rising profitability, reducing feed cost, decreasing GHG, managing the water and energy consumption, etc., by maximizing resilience and sustainability in dairy farms. Additionally, the results allow also for identifying the prospective measures for improving resilience and sustainability. Through results analysis, a strategy for developing resilience and sustainability can be well defined. Furthermore, the current research can be extended by integrating the model with life cycle assessment results, another producer support policies, dairy farms' capacity expansions and could also be applicable to other forms of agricultural systems by a bit changes in the decision variables and model parameters.
4- This data was gathered based on non-random sampling. Because, in non-random sampling, the sample individuals are selected among individuals who have a defined characteristics and based on researcher's opinion. The proposed model is designed for a sample dairy unit. In other words, the data obtained from non-random sampling were used only to determine the status of the studied samples.
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