Changing environmental conditions and the need to reduce the environmental impact of food systems highlight the importance of analyzing the direct and indirect environmental impacts of agricultural production systems. This paper embraces a Life Cycle Assessment (LCA) approach Using a cradle to farm gate framework to analyze a range of environmental interactions of almond production in three climatic regions - Mediterranean, semi-arid, and arid. Data was collected from 94 farmers across the state of Israel, representing 75 % of the country's almond orchard area.
View Article and Find Full Text PDFBackground: Environmental concerns are driving the call for adoption of alternative nonchemical weeding approaches. This study aimed to develop and evaluate a novel, precise, low-energy electrophysical treatment weeding systems and to provide new insight into their control mechanism. Two electrophysical treatment systems, based on AC (2.
View Article and Find Full Text PDFChemical weed-control is the most effective practice for wheat, however, rapid evolution of herbicide-resistant weeds threat food-security and calls for integration of non-chemical practices. We hypothesis that integration of alternative GA-responsive dwarfing genes into elite wheat cultivars can promote early vigor and weed-competitiveness under Mediterranean climate. We develop near-isogenic lines of bread wheat cultivars with GAR dwarfing genes and evaluate them for early vigor and weed-competitiveness under various environmental and management conditions to identify promising NIL for weed-competitiveness and grain yield.
View Article and Find Full Text PDFEffective control of the parasitic weed sunflower broomrape ( Wallr.) can be achieved by herbicides application in early parasitism stages. However, the growing environmental concerns associated with herbicide treatments have motivated the adoption of precise chemical control approaches that detect and treat infested areas exclusively.
View Article and Find Full Text PDFBackground: Weed/crop classification is considered the main problem in developing precise weed-management methodologies, because both crops and weeds share similar hues. Great effort has been invested in the development of classification models, most based on expensive sensors and complicated algorithms. However, satisfactory results are not consistently obtained due to imaging conditions in the field.
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