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Contemplation of potential strategies to adapt to a changing and variable climate in agricultural cropping areas depends on the availability of geo-information that is at a sufficient resolution, scale and temporal length to inform these decisions. We evaluated the efficacy of creating high-resolution, broad-scale indicators of yield from simple models that combine yield mapping data, a precision agriculture tool, with the normalised difference vegetation index (NDVI) from Landsat 5 and 7 ETM+ imagery. These models were then generalised to test its potential operationalisation across a large agricultural region (>1/2 million hectares) and the state of South Australia (>8 million hectares). Annual models were the best predictors of yield across both areas. Moderate discrimination accuracy in the regional analysis meant that models could be extrapolated with reasonable spatial precision, whereas the accuracy across the state-wide analysis was poor. Generalisation of these models to further operationalise the methodology by removing the need for crop type discrimination and the continual access to annual yield data showed some benefit. The application of this approach with past and contemporary datasets can create a long-term archive that fills an information void, providing a powerful evidence base to inform current management decisions and future on-farm land use in cropping regions elsewhere. [ABSTRACT FROM AUTHOR]

Wheat dominates the Australian grain production market and accounts for 10–15% of the world's 100 million tonnes annual global wheat trade. Accurate wheat yield prediction is critical to satisfying local consumption and increasing exports regionally and globally to meet human food security. This paper incorporates remote satellite-based information in a wheat-growing region in South Australia to estimate the yield by integrating the kernel ridge regression (KRR) method coupled with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the grey wolf optimisation (GWO). The hybrid model, 'GWO-CEEMDAN-KRR,' employing an initial pool of 23 different satellite-based predictors, is seen to outperform all the benchmark models and all the feature selection (ant colony, atom search, and particle swarm optimisation) methods that are implemented using a set of carefully screened satellite variables and a feature decomposition or CEEMDAN approach. A suite of statistical metrics and infographics comparing the predicted and measured yield shows a model prediction error that can be reduced by ~20% by employing the proposed GWO-CEEMDAN-KRR model. With the metrics verifying the accuracy of simulations, we also show that it is possible to optimise the wheat yield to achieve agricultural profits by quantifying and including the effects of satellite variables on potential yield. With further improvements in the proposed methodology, the GWO-CEEMDAN-KRR model can be adopted in agricultural yield simulation that requires remote sensing data to establish the relationships between crop health, yield, and other productivity features to support precision agriculture. [ABSTRACT FROM AUTHOR]

Precision agriculture (PA) offers opportunities for the development of new approaches to on-farm experimentation to assist farmers with site-specific management decisions. Traditional agricultural experiments are usually implemented in fields with the least possible soil heterogeneity under the assumption that responses to inputs and inherent variation of the soil are additive components of yield variation. However, because the soil in typical fields is not homogeneous, PA has much to offer. Farmers faced with variable conditions need to optimize their management to the variation over space and time on their farm, a problem that is not solved by conventional approaches to experimentation. New designs for on-farm experiments were developed in the 1990s for cereal production in which the whole field was used for the experiment rather than small plots. We explore the extension of this type of experiment to a vineyard in the Clare Valley of South Australia aiming to evaluate options to increase grape yield and vine vigour. Manually sampled indices of vine performance measured on georeferenced ‘target’ grapevines were analysed geostatistically. The major advantage of such an approach is that the spatial variation in response to experimental treatments can be examined. Linear models of coregionalization, pseudo cross-variograms and standardized ordinary cokriging are used to map treatment responses over the experimental area and also the differences between them. The results indicate that both treatment responses and the significance of differences between them are spatially variable. Thus, we conclude that whole-of-block on-farm trials are useful in vineyards. [ABSTRACT FROM AUTHOR]

• High-dimensional multivariate spatial/geostatistical data modelling in precision agriculture. • Localised farm management decisions using production dissimilarities. • Toolbox using open source R programming language. Farm management decisions based on multiple treatments or yield differences with measurable statistical uncertainty are effective and popular. The "oft" (on-farm-tools) software developed in this study provides an easy access to identify local neighbourhood points for non-collocated multiple treatment response differences. Methods for analysing multiple treatments based on multivariate geostatistics are generally computationally expensive for relatively large samples, such as data coming from modern sensors. Localised methods for multiple treatments reduce the computational complexity and can address local variability; however, their performance depends on the choice of the number of neighbours. A common practice for localised methods is to pre-specify a neighbourhood radius or number of neighbourhood samples based on analyst or farmer experience while tolerating the computational costs. Hence, introducing the "oft" package, we propose an adaptive neighbourhood point algorithm to identify the neighbouring samples for the localised analysis in order to achieve a good balance between computation time and accuracy. We explore an experimental case study dataset obtained from a vineyard in Langhorne Creek, South Australia, which is a particular application of the general problem of the neighbourhood selection for localised multivariate geostatistics. [ABSTRACT FROM AUTHOR]