Farmer attitudes to the use of sensors and automation in fertilizer decision-making: nitrogen fertilization in the Australian grains sector

被引:58
作者
Bramley, R. G. V. [1 ]
Ouzman, J. [1 ]
机构
[1] CSIRO, Waite Campus,Locked Bag 2, Glen Osmond, SA 5064, Australia
关键词
Yield mapping; Remote and proximal sensing; Technology adoption; Variable rate application; WATER REPELLENT SANDS; PRECISION AGRICULTURE; BIG DATA; CROP; MANAGEMENT; INDUSTRY; ADOPTION; SUPPORT; SOIL;
D O I
10.1007/s11119-018-9589-y
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
A survey of Australian grain growers was conducted to gauge grower attitudes to crop and soil sensing and their role in nitrogen fertilizer management. The technologies considered were yield monitors, remote and proximal crop sensing, high resolution soil survey, soil moisture sensing and digital elevation models (DEM). Whereas Australian grain growers have readily adopted machine guidance and autosteer, and a majority have access to yield monitoring, the rate of use of many crop and soil sensors remains comparatively low. However, the survey results suggest a positive effect on sensor adoption through present use of yield mapping. Access to yield maps was significantly associated with the use of remotely sensed imagery, high resolution soil survey, soil moisture sensing, DEM and variable rate application of fertilizers and/or soil amendments. There is some support for proximal crop sensing, albeit with low present rates of use; the use of soil water sensors and DEM is presently very low. For the further development of precision agriculture (PA), the results make clear that expending effort in enhancing the adoption and use of yield maps would be valuable as a lever to gain buy-in' from growers to sensing and PA more broadly. Since growers use many sources of information to support fertilizer decision-making, any new fertilizer decision aid needs to establish a point of difference from, but be complementary to, existing tools. One way of achieving this would be to use sensors, supported by locally derived algorithms, as a key input to fertilizer decision support.
引用
收藏
页码:157 / 175
页数:19
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