Contribution of precision agriculture on assessing the spatial variability of yield and quality in a commercial wheat field

Abstract

Trabajo presentado en Digital Rural Future Conference 2014, celebrado en Australia en junio de 2014.The agricultural research sector is working to develop new technologies and management knowledge to sustainably increase food productivity, to ensure global food security and decrease poverty. Wheat is one of the most important crops into this scenario, be ing between the two most important cereal commodities produced worldwide . Precision Agriculture (PA) and Remote Sensing (RS) technologies can contribute to increase wheat yield and quality sustainably. For this reason, CIMMYT ’s research agenda aim s at deve lop ing new crop management pr actices using PA technologies . As part of these efforts, an experiment has been established on a wheat farm’s field in the Yaqui Valley , in north western Mexico , sowed on January 2014 . Our hypothesis is that it is possible to as sess the key factors affect ing wheat yield and quality variability, aiming to detect the correctable and uncorrectable main factors . We are also explor ing the potential for wheat growers and processors to adopt a selective harvesting strategy based on grain protein content , extracting greater value from the raw product. Prior t o sowing we carried out a high resolution soil survey using an electromagnetic induction sensor – EM38 , mounted in a wood sled and tractor dragged through the field ; fol lowed by a targeted soil sampling at two depths (0 - 0.3 and 0.3 - 0.6 m ) for physical and chemical soil properties analysis. A weekly flight campaign took place fr o m GS31 stage until harvest, using high resolution airborne hyperspectral and thermal imaging se nsors flying at 600 m above ground , with ground resolution of 0.5 m (hyperspectral) and 0.75 m (thermal) . Yield and quality monitoring will take place during harvest . We expect to assess the spatial variability of yield and quality using the proximal and remote high resolution data , exploring the possibility of a logistic strategy for selective harvesting ; explor ing also the use of those data for a better crop managementN