On the Economics of Precision Agriculture: Technical, Informational and Environmental Aspects

The paper presents an integrated framework of biophysical and economic modelling as a novel approach towards precision agriculture research. A theoretical economic model determining the optimal number of precision agriculture management units within a given field of land is presented. The model is expanded to account for the value of the research information provided be the precision agriculture researchers. Since the inherent environmental values associated with precision agriculture are often omitted from the economic analysis, an attempt is made to incorporate these values into the model. The versions of the model are empirically tested using the data available.economics, precision agriculture, environment, information, Agribusiness, Environmental Economics and Policy,

Soil properties drive microbial community structure in a large scale transect in South Eastern Australia

Soil microbial communities directly affect soil functionality through their roles in the cycling of soil nutrients and carbon storage. Microbial communities vary substantially in space and time, between soil types and under different land management. The mechanisms that control the spatial distributions of soil microbes are largely unknown as we have not been able to adequately upscale a detailed analysis of the microbiome in a few grams of soil to that of a catchment, region or continent. Here we reveal that soil microbes along a 1000 km transect have unique spatial structures that are governed mainly by soil properties. The soil microbial community assessed using Phospholipid Fatty Acids showed a strong gradient along the latitude gradient across New South Wales, Australia. We found that soil properties contributed the most to the microbial distribution, while other environmental factors (e.g., temperature, elevation) showed lesser impact. Agricultural activities reduced the variation of the microbial communities, however, its influence was local and much less than the overall influence of soil properties. The ability to predict the soil and environmental factors that control microbial distribution will allow us to predict how future soil and environmental change will affect the spatial distribution of microbes

Engaging employers, graduates and students to inform the future curriculum needs of soil science

This paper reports on the findings of a project to investigate the future needs of a soil science curriculum to produce work-ready graduates. Soil scientists are expected to deal with increasingly complex problems and graduates are required to have not only have well developed soil science knowledge and skills, but can also work between and across other disciplines communicate their findings appropriately. Survey results obtained from current student, graduates and employers of soil science indicated some areas of discipline knowledge that need to be addressed, as well as more emphasis on developing critical thinking and problem solving skills. Employers also expressed the desire to not only provide advice on curriculum change but a willingness to be involved in the learning environment. Using problem based learning as the scaffold an example of how industry maybe engaged is provided. Issues are raised around the need to align the graduate outcomes for soil science with Threshold Learning Outcomes for Science and Agriculture and the need for a core-body of knowledge (CBoK) that characterise graduates with soil science knowledge. As a result of widespread stakeholder consultations during the project a set of soil science teaching principles was developed (Field et al., 2011). Field, D. J., Koppi, A. J., Jarrett, L. E., Abbott, L. K., Cattle, S. R., Grant, C. D. McBratney A. B., Menzies N. W., Weatherly A. J. (2011). Soil Science Teaching Principles. Geoderma, 167-168, 9-14

Guidelines for online learning in soil science: A synthesis of ideas from academics, students and employers

As part of an ALTC-supported curriculum development project, we engaged teaching staff, employers, current students and recent graduates in the discipline of soil science to develop a set of guidelines for online learning in our discipline. During a one-day Forum, three experienced practitioners in online learning design in engineering, science and health presented and discussed their approaches with the forum participants. The forum attendees then developed guidelines for online learning in soil science based on their personal experiences together with the presentations. The resulting guidelines were compared with the literature and a very good match found in assessment, content, communication and feedback, motivation and groupwork. Two additional aspects that apply particularly to teaching soil science in Australia were identified, namely the importance of defining agreed outcomes that take into account regional differences across academic institutions and accommodating the broad range of prior knowledge that students of soil science bring to online courses

Spatial soil information systems ans spatial soil inference systems: Perspectives for digital soil mapping

International audienceGiven the relative dearth of, and the huge demand for, quantitative spatial soil information, it is timely to develop and implement methodologies for its provision. We suggest that digital soil mapping, which can be defined as the creation, and population of spatial soil information systems (SSINFOS) by the use of field and laboratory observational methods, coupled with spatial and non-spatial soil inference systems, is the appropriate response. Problems of large extents and soil-cover complexity and coarse resolutions and short-range variability representation carry over from conventional soil survey to digital soil mapping. Meeting users' requests and demands and the ability to deal with spatially variable and temporally evolving datasets must be the key features of any new approach. In this chapter, we present a generic framework that recognises the procedures required. Within quantitatively defined physiographic regions, SSINFOS must be populated and spatial soil inference systems (SSINFERS) must be developed. When combined this will allow users to derive the data they require. Further work is required on the development of these systems, and on the data requirements, the optimal forms of inference and the appropriate representation of the products of digital soil mapping

A platform to interpret soil attributes to support profitable farming systems.

This collection contains data on some estimated and mapped soil physical properties such as: clay, sand, pH, Cation Exchange Capacity (CEC), and organic carbon (OC) generated through predictive models using a developed framework that quantitatively assess the accuracy of data collected with proximal soil sensors and spectroscopic techniques such as visible near-infrared visNIR and portable X-ray Fluorescence (pXRF) spectroscopy. First, tools were provided to assist in the collation of freely available data such as elevation and satellite derived data as well as on-farm data produced with electromagnetic induction (EM) and gama radiometricts. Second, an automated site selection software was developed to collate and process covariate data to identify 25 samples sites across L'lara, a mixed-farming property located ~11 km Narrabri in NSW in 2020. Fieldwork and example mapping soil properties were conducted at L'lara using visNIR spectrocopy and pXRF spectrometers. A conditioned Latin hypercube sampling design was chosen to sample the distribution of the covariate space under both cropping and pasture on the 1,850 ha property. Covariate data supplied to the software included on-site EM, gamma radiometrics, yield, soil legacy data, plus elevation and satellite derived data. A soil inference system (SPEC-SINFERS) was developed, using other spectrally active properties through pedotransfer functions (PTFs) to predic further properties such as available water capacity (AWC) from sensor predicted properties. A large spectral library was construted with > 8,000 pre-existing soil samples predominantly from grain-growing regions of NSW and additional accession from Qld., Victoria and Tasmania and fieldwork data. Multi-depth mapping of soil properties and attributes (Depth-to pH constraint) was also investigated to provide agronomic interpretations to the produced soil maps and correlations with available yield data. The accuracy of mapped soil properties was tested under data-rich and data-poor scenarios. Calibration and validations of each scenario were made with laboratory data, available covariate data (elevation, satellite image) and with/without on-farm colleted EM and gamma data. RMSE was used in percentage change as reference to other studies. Mapped yield products revealed significant correlations for canola, chickpea and wheat in two paddocks over two growing seasons. Datasets generated for this project are stored in the RDS - GRDC_US00087 (\\shared.sydney.edu.au\research-data\PRJ-GRDC_US00087). Please contact Prof. Alex McBratney to request access to them

Estimation and Potential Improvement of the Quality of Legacy Soil Samples for Digital Soil Mapping

Legacy soil data form an important resource for digital soil mapping and are essential for calibration of models for predicting soil properties from environmental variables. Such data arise from traditional soil survey. Methods of soil survey are generally empirical and based on the mental development of the surveyor, correlating soil with underlying geology, landforms, vegetation and air-photo interpretation. There are no statistical criteria for traditional soil sampling, and this may lead to biases in the areas being sampled. The challenge is to use legacy data for large-area mapping (e.g. national or continental) as funds are limited to resample large areas. The problem is then to assess the reliability and quality of the legacy soil databases that have been mainly populated by traditional soil survey, and if there is a possibility of additional funding for sampling, where should new sampling units be located. This additional sampling can be used to improve and validate the prediction model. Latin hypercube sampling (LHS) has been proposed as a sampling design for digital soil mapping when there is no prior sample. We use the principle of hypercube sampling to assess the quality of existing soil data and guide us to the area that needs to be sampled. First an area is defined and the empirical environmental data layers or covariates are identified on a regular grid. The existing soil data is matched with the environmental variables. The HELS spell out algorithm is used to check the occupancy of the legacy sampling units in the hypercube of the quantiles of the covarying environmental data1 . This is to determine whether legacy soil survey data occupy the hypercube uniformly or if there is over- or under-observation in the partitions of the hypercube. It also allows posterior estimation of the apparent probability of sample units being surveyed. From this information we can design further sampling. The methods are illustrated using legacy soil samples from Edgeroi, New South Wales, Australia, and from a large part of the Danube Basin.JRC.H.7-Land management and natural hazard

On the Economics of Precision Agriculture: Technical, Informational and Environmental Aspects

The paper presents an integrated framework of biophysical and economic modelling as a novel approach towards precision agriculture research. A theoretical economic model determining the optimal number of precision agriculture management units within a given field of land is presented. The model is expanded to account for the value of the research information provided be the precision agriculture researchers. Since the inherent environmental values associated with precision agriculture are often omitted from the economic analysis, an attempt is made to incorporate these values into the model. The versions of the model are empirically tested using the data available

Digital soil mapping an introductory perspective PREFACE

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