A simulation-based approach for evaluating the effects of farm type, management, and rainfall on the water footprint of sheep grazing systems in a semi-arid environment

Toro-Mújica, Paula; Aguilar, Carlos; Vera, Raül; Cornejo, Karen

doi:10.1016/j.agsy.2016.07.011

Cited by 9 publications

(7 citation statements)

References 55 publications

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“…GYWF is estimated following the standard computational methods [15]: (18) α is the leaching-runoff fraction and assumed as 10% following previous practices; AR is the rate of nitrogen application to the field per hectare, kg/ha; C max is the maximum acceptable concentration; C max is the concentration in natural water, assumed to be 0 mg/L.…”

Section: Estimation Of Crop Water Footprint (Cwf) Componentsmentioning

confidence: 99%

“…The crop water footprint (CWF) is the volume of water both consumed and affected by agricultural pollutants, such as chemical fertilizer and pesticide, during the crop season [17] and contains blue (the volume of effective precipitation consumed during crop season), green (the volume of irrigation consumed in crop production) and gray (the volume of freshwater that is required to assimilate the load of pollutants) components [15]. Compared with traditional methods, the progress of CWF is to distinguish between the attributes of blue and green water resources and quantify the negative impact of agricultural production on the water environment [18]. Numerous empirical studies have been conducted on the water footprint and efficiency in major crop production systems at global, national and regional scales [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

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Water Use Efficiency and Sensitivity Assessment for Agricultural Production System from the Water Footprint Perspective

Wang

Cao

2020

Sustainability

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The increasing shortage of water resources and the growing demand for crops make water use efficiency a decisive factor for the sustainable and healthy development of the agricultural system. In order to evaluate agricultural water use efficiency from the water footprint perspective, the current study constructed the comprehensive water efficiency (CWE) index based on eight single agricultural water use efficiency performance parameters. The water resources utilization and efficiency in the wheat production system of China from 2006 to 2015 were analyzed and the sensitivity of single indices for CWE was identified. The results show that the national crop water footprint (CWF) for wheat production was estimated to be, including 46.3% blue, 36.6% green and 17.0% blue components, respectively. The spatial distribution patterns of water use efficiency performance indices were different. CWE of the country was 0.387, showing an upward trend over time and decreased from the southeast to the northwest geographically. Crop water productivity (CWP), productive water ratio (PWR) and rainwater consumption ratio (RCR) turned out to be the first three sensitive parameters for CWE in China. The improvement of China’s overall CWE relied on reducing inefficient blue-green water use and increasing the output capacity for per unit water. Advanced agricultural water-saving technologies were in high need for goal achievement, especially for the Huang-Huai-Hai plain, which held more than 70% of Chinese wheat production and CWF. The results provide support for efficient utilization and sustainable development of water resources in the agricultural system.

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Section: Estimation Of Crop Water Footprint (Cwf) Componentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Water Use Efficiency and Sensitivity Assessment for Agricultural Production System from the Water Footprint Perspective

Wang

Cao

2020

Sustainability

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“…Minor differences in survey format were apparent between years; however, 72 variables were common to all, 14 of which were qualitative (some examples in Table A1). These and 43 additional variables (calculated from the original variables), were used in the analyses [37,38] (Tables 1 and 2). The surveys' samples were randomly stratified and proportional to herd size, according to census data.…”

Section: Data Sourcesmentioning

confidence: 99%

“…The Chi-square test was used to determine the relationship between quantitative and qualitative variables and to select between a multiple correspondence and a factor analysis [24]. Principal components with varimax rotation were extracted for the factor analysis [38,40]. Sampling adequacy was assessed with the Kaiser-Meyer-Olkin test and Bartlett's sphericity test [46].…”

Section: Statistical Analysesmentioning

confidence: 99%

Trends and Drivers of Change of Pastoral Beef Production Systems in a Mediterranean-Temperate Climate Zone of Chile

Toro-Mújica

Vera

Vargas‐Bello‐Pérez

et al. 2019

Animals

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Simple Summary: Livestock production systems show modifications over time due to both internal and external variables. The relationships between internal variables allow the definition of typological groups of production systems. Knowing the typological groups and their temporal evolution allows the identification of continuity strategies at the private and public levels. With this aim and using data from livestock surveys conducted in 2009, 2011, 2013 and 2015, four typological groups of beef production in a Mediterranean-temperate climate zone of Chile were identified. The typological groups differed in size, productive orientation, breeds used, and response to changes in external variables, among other aspects. These groups require different continuity strategies, which include, their sustainable intensification and the need to focus on pastoral production using low external inputs to enhance the production of "natural" beef for high-value niche markets. Abstract:The present study used surveys of the cattle sector over the period of 2009-2015 to develop a typology of cattle farms to evaluate their evolution over time and to identify variables that may be associated with systems' adaptive changes and continuance. Four groups of farms were defined using multivariate analyses as follows: Group I are small calf-cow operations using non-specialized beef breeds; Group II is similar to Group I but employs specialized beef breeds; Group III is dedicated to finishing cattle, and Group IV are larger farms (>1000 animals) with a complete cycle of breeding and fattening. In general, beef cattle production in the temperate-Mediterranean Southern Zone of Chile is declining in response to the opening up of the economy that allows for ample imports, the high opportunity cost of land, and recurrent droughts associated with climate change. Current policies and regulations have modified farms' businesses models depending on their ease of access to markets, farm size and financial capacity. The defined groups require different development paths and strategies. Sustainable intensification is an alternative strategy for farms in Group I and II, particularly if they were to contract the finishing stage of their cattle with Group III farms. In contrast, it is suggested that Group IV farms concentrate on pastoral production using low external inputs to enhance the production of "natural" beef for high-value niche markets, with positive externalities.

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“…However, information on how much water is consumed per kilogram of eggs produced, which should be estimated and given to the public and water resource managers, is still limited. The WF of animal products varies largely, due to differences in farming systems and the species of animals [12,23,24]. Research studies on assessing water use of different species of laying hens in a parent-stock layer breeder farm are scanty.…”

Section: Introductionmentioning

confidence: 99%

Water Footprint Assessment of Eggs in a Parent-Stock Layer Breeder Farm

Xing

Zheng

et al. 2019

Water

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Egg production and consumption of eggs in China account for about 40% of the global total, and this constitutes a significant demand for water resources. The shortage of water resources in China means egg production in this country has serious water challenges. However, there are few studies concerning water use in egg production. In this study, the water footprint network (WFN) methodology was applied to analyze the water footprint (WF) of intensive egg production using a typical parent-stock layer breeder farm in North China as a model, which raises 208,663 layer breeders and produces about 2791.39 t eggs per year. The feed and water consumption over 353 days were collected for analysis, and the water footprint of chicken eggs was estimated at the farm level. The water footprint of eggs in a parent-stock layer breeder farm in China was therefore assessed in detail for the first time, and suggestions are put forward to reduce the egg water footprint from the perspective of the production chain and improving water use efficiency on the farm. The results show that (1) the green WF of eggs (water volume/egg weight) ranged from 1.917 to 2.114 m3/kg, the blue WF was 0.584 to 0.644 m3/kg and the grey WF was 0.488 to 0.538 m3/kg; (2) the indirect WF generated by feed contributed over 99.8% of the total; (3) eggs laid by Hy-line Brown hens have a lower WF than those from Hy-line Sonia hens, and the studied layer breeder farm had a higher WF than the global average based on the literature. In this paper, the variation of the WF was also analyzed, and some advice on water management for layer farms in China is provided.

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A simulation-based approach for evaluating the effects of farm type, management, and rainfall on the water footprint of sheep grazing systems in a semi-arid environment

Cited by 9 publications

References 55 publications

Water Use Efficiency and Sensitivity Assessment for Agricultural Production System from the Water Footprint Perspective

Water Use Efficiency and Sensitivity Assessment for Agricultural Production System from the Water Footprint Perspective

Trends and Drivers of Change of Pastoral Beef Production Systems in a Mediterranean-Temperate Climate Zone of Chile

Water Footprint Assessment of Eggs in a Parent-Stock Layer Breeder Farm

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