Lessons from nearly 20 years of Precision Agriculture research, development, and adoption as a guide to its appropriate application

Abstract

Precision Agriculture (PA) is an all-encompassing term given to the use of a suite of technologies that promote improved management of agricultural production through recognition that the potential productivity of agricultural land can vary considerably, even over very short distances (a few m). It can be regarded as a means of increasing the chance that the right crop management strategies are implemented in the right place at the right time. Numerous examples exist of the successful application of PA to various cropping systems around the world, in many cases supported by a burgeoning PA literature. However, the rate of adoption by growers of many crops remains low and, in some industries, is negligible. One such example is the Australian sugar industry, in spite of its relatively high rate of adoption of controlled traffic and the ready access that growers have to supporting infrastructure such as local GPS base stations. However, the Australian sugar industry is now seeking an informed basis from which to make decisions as to appropriate investment in PA, whether these be in terms of pragmatic application by growers, the level of involvement (if any) by millers, or with respect to research to facilitate such adoption. A part of acquiring this informed view of PA is to look at its application in other cropping systems. This review therefore examines PA research and application in a range of cropping systems from around the world and considers the key drivers of variability in these production systems. Constraints to the adoption of PA and its likely economic benefits are also considered in light of experiences from around the world. It is concluded that sugarcane production is ideally suited to the adoption of PA. Like other broadacre systems, such as cereal production, the opportunity exists to target the management of inputs to production. However, the vertically integrated nature of the sugar industry and existence of a potentially significant crop quality imperative also present opportunities for targeted strategies such as selective harvesting, as used in the wine industry. Thus, to get the best result from adoption of PA, the sugar industry will need to consider it as a tool for optimising management of the production of sugar, as opposed to solely an avenue for improving the agronomic management of sugarcane. Several recommendations are made as to how this adoption might be supported.

Sistemas de Información enfocados en tecnologías de agricultura de precisión y aplicables a la caña de azúcar, una revisión

“…La importancia de resaltarlos recae en conocer qué impedimentos puede llegar a afrontar dicha implementación en el entorno colombiano. Los desafíos más importantes y estrategias para enfrentarlos se destacan a continuación [3,[6][7][8]:…”

“…La aproximación a un modelo de gestión, monitorización, simulación y comunicación se muestra en la figura 3, en donde se observa un conjunto de procesos divididos en dos partes para facilitar la toma de decisiones, gestión y monitorización. Fuente: [28] 1 Farmer's problem unstructured & situation under-specified 4 Flexible simulator 3 Problem situation better specified via on-farm monitoring and experimentation 2 Cooperative learning by farmers, advisers, and researchers with different world views 6 Researcher/Adviser simulating production system 5 Characterisation of soils 12 Soil and wather databases 11 Improve simulator 9 Management to improve the situation 8 En [29] se describe un análisis de datos para experimentos en AP, incluyendo aplicaciones para el caso de uso de la caña de azúcar. Este análisis se basa en la representación de técnicas de adquisición de datos, la estimación de ruido en cada medición, el análisis de autocorrelación espacial para cada atributo y el aná-lisis de componentes principales (PCA: Principal Component Analysis) de los mismos.…”

“…El objetivo general de la agricultura de precisión es integrar la gestión espacial y temporal de la producción a través de Sistemas de Posicionamiento Global (GPS, Global Positioning Systems), Sistemas de Información Geográfica (SIG), tecnologías de sensores y mapas de terrenos. Lo anterior con el fin de aumentar la rentabilidad y productividad de los agricultores mediante la generación de mapas de productividad en función de las condiciones agroclimáticas del terreno [3].…”

Remote Sensing Derived Phenological Metrics to Assess the Spatio-Temporal Growth Variability in Cropping Fields

“…Precision agriculture (PA) recognizes and manages intra-field spatial variability with the desired outcome of increasing profitability and reduced environmental impact [1]. Site Specific Crop Management (SSCM) is a form of PA that focuses on managing Spatial crop yield variability through matching agricultural inputs with the site potential.…”

Exploring Precision Farming Scenarios Using Fuzzy Cognitive Maps

“…There is a number of studies that surveyed a variety of factors (socio-economic, agro-ecological, institutional, technological or behavioral), which affect the uptake of PA technology by farmers [2,[11][12][13][14][15].…”