G. A. Wood

Simulation scenarios of spatio-temporal arrangement of crops at the landscape scale

The spatial and temporal arrangement of crops is a conspicuous feature of rural landscapes. It has been identified as an important factor in many environmental issues, such as the coexistence of genetically modified (GM) and non-GM crops, and the mitigation of soil erosion. This paper examines a scenario-based approach for rapid generation and screening of crop allocations that meet users constraints without requiring mechanistic modelling. LandSFACTS (Landscape Scale Functional Allocation of Crops Temporally and Spatially) is a software application specifically designed to simulate such crop arrangement scenarios, whilst ensuring both spatial and temporal coherence with regard to the initial constraints. The software uses an empirical approach to allocate crops to fields (polygons in vector format) over a sequence of years, using a stochastic process (Markov chains) and rule-based constraints. Crop rotations are represented by transition probabilities complemented by other temporal constraints such as return period or prohibited sequences. Further spatial and temporal constraints on crop arrangement can be applied through separation distances, yearly proportions, and the application of statistical tests. The software outputs a crop allocation solution with a crop for every field for every year, respecting all user-defined constraints; the range of potential solutions can then be explored through multiple model runs. Metrics based upon the difficulty of obtaining such an allocation from the initial constraints are also generated. A case study is provided to demonstrate the use of combined agronomic and environmental criteria for exploring GM crop coexistence at the landscape scale.

Calibration Methodology for Mapping Within-field Crop Variability using Remote Sensing

A successful method of mapping within-field crop variability of shoot populations in wheat (Triticum aestivum) and barley (Hordeum vulgare L.) is demonstrated. The approach is extended to include a measure of green area index (GAI). These crop parameters and airborne remote sensing measures of the normalised difference vegetation index (NDVI) are shown to be linearly correlated. Measurements were made at key agronomic growth stages up to the period of anthesis and correlated using statistical linear regression based on a series of field calibration sites. Spatial averaging improves the estimation of the regression parameters and is best achieved by sub-sampling at each calibration site using three 0·25 m2 quadrats. Using the NDVI image to target the location of calibration sites, eight sites are shown to be sufficient, but they must be representative of the range in NDVI present in the field, and have a representative spatial distribution. Sampling the NDVI range is achieved by stratifying the NDVI image and then randomly selecting within each of the strata; ensuring a good spatial distribution is determined by visual interpretation of the image. Similarly, a block of adjacent fields can be successfully calibrated to provide multiple maps of within-field variability in each field using only eight points per block representative of the NDVI range and constraining the sampling to one calibration site per field. Compared to using 30 or more calibration sites, restricting samples to eight does not affect the estimation of the regression parameters as long as the criteria for selection outlined in this paper is adhered to. In repeated tests, the technique provided regression results with a value for the coefficient of determination of 0·7 in over 85% of cases. At farm scale, the results indicate an 80–90% probability of producing a map of within crop field variability with an accuracy of 75–99%. This approach provides a rapid tool for providing accurate and valuable management information in near real-time to the grower for better management and for immediate adoption in precision farming practices, and for determining variable rates of nitrogen, fungicide or plant growth regulators.

Benefits from Application of Ground Based Remote Sensing Systems in Winter Wheat Nitrogen Management in Europe

Nitrogen management is a crucial issue in terms of environmental and economical efficiency for winter wheat husbandry. Precision Agriculture in particular Remote Sensing has been used to determine the variability of the crop. Despite the presence of some commercial applications of the satellite and airborne techniques, the ground based remote sensing systems (GBRSS) offer advantages in terms of availability. The most common passive GBRSS in Europe is the Yara N sensor which has limitations in poor light conditions. Active sensors, using their own energy sources, are now available in the market e.g. the Crop Circle (Holland Scientific) and the Yara N sensor ALS.

A Novel Quantitative Method for the Determination of Wear in an Installed Synthetic Turf System

This study focuses on the initial development of an image analysis methodology for quantifying the wear and degradation of synthetic sports turf, post installation, where the carpet/infill system is subjected to systemic abrasion and wear from play and maintenance. The pilot study images the surface of polypropylene fibres, which have been agitated with differing sand infill types, with a scanning electron microscope. The resultant images were analysed to determine the degradation of the extrusion features evident in virgin fibre, and it was found that there was significant, quantifiable wear of the turf fibres after seven days with all test sands. The image data for fibres between 7 and 28 days was dependent upon sand type. Further development of the technique is required for determining the next stage of wear — characterized by pitting of the fibre surface by the sand.