Strawberry fields in California (9,500 ha annually) are pre-plant fumigated with methyl bromide and chloropicrin to prevent serious soil pest and disease problems. Although soil fumigation with methyl bromide has ensured stability of strawberry production, its use is being discontinued because of its effect on stratospheric ozone. The likely short-term alternatives such as 1,3-dichloropropene, chloropicrin, and metham sodium, although not ozone depleters, are potentially hazardous to the environment and humans if applied improperly. Water-soluble formulations of alternative fumigants can be applied through drip irrigation systems established to irrigate crops. In comparison to conventional shank methods of injection, application of soluble formulations through drip irrigation systems would be economical and environmentally friendly, reduce worker exposure, and allow for simultaneous or sequential application of a combination of fumigants. This paper discusses techniques developed to apply alternative fumigants through drip irrigation systems, and reviews ongoing studies to determine optimum application rates, soil conditions, plastic mulches, and amount of irrigation water used to apply these alternative fumigants.
tially variable soil properties that affect crop yield to better optimize crop productivity and to maintain the Crop yield inconsistently correlates with apparent soil electrical sustainability of agriculture. conductivity (EC a ) because of the influence of soil properties (e.g., Site-specific crop management is the management of salinity, water content, texture, etc.) that may or may not influence soils, pests, and crops based on spatial variations within yield within a particular field and because of a temporal component a field (Larson and Robert, 1991). Site-specific manageof yield variability that is poorly captured by a state variable such as ment utilizes rapidly evolving electronic information tech-EC a . Nevertheless, in instances where yield correlates with EC a , maps of EC a are useful for devising soil sampling schemes to identify soil nologies to modify land management in a site-specific properties influencing yield within a field. A west side San Joaquin Val-manner as conditions change spatially and temporally ley field (32.4 ha) was used to demonstrate how spatial distributions (van Schilfgaarde, 1999). The aim of precision agriculof EC a can guide a soil sample design to determine the soil properties ture is to improve management to increase profitability, influencing seed cotton (Gossypium hirsutum L.; 'MAXXA' variety) increase crop productivity, sustain the soil-plant-water yield. Soil sample sites were selected with a statistical sample design environment, and/or reduce detrimental environmental utilizing spatial EC a measurements. Statistical results are presented impacts (Atherton et al., 1999). from correlation and regression analyses between cotton yield and Precision agriculture is a technologically driven systhe properties of pH, B, NO 3 -N, Cl Ϫ , salinity, leaching fraction (LF), tem (van Schilfgaarde, 1999). First conceived in the mid gravimetric water content, bulk density, percentage clay, and satura-1980s, the technological pieces needed to bring precision tion percentage. Correlation coefficients of Ϫ0.01, 0.50, Ϫ0.03, 0.25, agriculture into its own began to fall into place in the 0.53, Ϫ0.49, 0.42, Ϫ0.29, 0.36, and 0.38, respectively, were determined. mid 1990s with the maturation of global positioning sys-A site-specific response model of cotton yield was developed based tems (GPS) and geographical information systems (GIS). on ordinary least squares regression analysis and adjusted for spatial These and other new technologies potentially provide autocorrelation using maximum likelihood. The response model indicated that salinity, plant-available water, LF, and pH were the most the ability to (i) quantify yield variability in small areas significant soil properties influencing cotton yield at the study site. of the field; (ii) quantify the spatial variability of soil The correlations and response model provide valuable information properties influencing yield; and (iii) adjust inputs such for site-specific management.as fertilizer, pesticide, and seeding rates based on knowledg...
A major opportunity exists in the transition from the disciplinary Millennium Development Goals (MDG's) to the more interdisciplinary Sustainable Development Goals (SDG's) to transform the dialogue around the environment from its current conceptualization as a constraining factor towards its utilization an enabling force for sustainable development.Agricultural ecosystems are the ecosystems closest to human well-being providing 31% of global employment, and the sustenance for the entirety of the global population. Despite this central contribution, agricultural systems or agroecosystems are responsible for driving significant environmental pressures while failing to provide sustainable diets for a world that is increasing either under-or malnourished. Agriculture's dual change is often presented as a trade-off between conservation and development objectives. However, an increasing body of research demonstrates that agricultural systems are both wholly dependent on, and potentially net providers of ecosystem services beyond food production. Recognizing this dual role allows for greater convergence between conservation and development objectives and leverages environmental management of agricultural system as a means to achieving global sustainability goals. We propose a simple framework for ecosystem services and resilience in agricultural landscapes to better capture and operationalize interactions between ecosystem services provided to agriculture, and those provided from agriculture. We discuss how such a perspective influences definitions of production ecosystem services and emphasize the need for greater focus on resilience and regulating services. We argue for better applications of resilience-based approaches and call for refocusing ecosystem service research on human well-being outcomes articulated in the social targets of the SDGs, in addition to the more traditional biophysical and conservation based outcomes.
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