Precision agriculture (PA) is the application of geospatial techniques and sensors (e.g., geographic information systems, remote sensing, GPS) to identify variations in the field and to deal with them using alternative strategies. In particular, high-resolution satellite imagery is now more commonly used to study these variations for crop and soil conditions. However, the availability and the often prohibitive costs of such imagery would suggest an alternative product for this particular application in PA. Specifically, images taken by low altitude remote sensing platforms, or small unmanned aerial systems (UAS), are shown to be a potential alternative given their low cost of operation in environmental monitoring, high spatial and temporal resolution, and their high flexibility in image acquisition programming. Not surprisingly, there have been several recent studies in the application of UAS imagery for PA. The results of these studies would indicate that, to provide a reliable end product to farmers, advances in platform design, production, standardization of image georeferencing and mosaicing, and information extraction workflow are required. Moreover, it is suggested that such endeavors should involve the farmer, particularly in the process of field design, image acquisition, image interpretation and analysis.

The application of small unmanned aerial systems for precision agriculture: a review

Implementing precision agriculture in the 21st century.

This chapter provides a comprehensive review of the literature relating to the impacts of grazing animals on the quality of soils, vegetation, and surface waters. It focuses on intensively managed grasslands where there is the greatest potential for these impacts to be observed. The chapter indicates that while well-managed grazing can be beneficial to the environment, intensively managed grazing can actually lead to the degradation of both the soil and vegetation of grassland environments. The various causes, forms, and consequences of this degradation are discussed in detail, and gaps in the knowledge are identified. The chapter highlights the need for recognition and quantification of the relationships between the on-site impacts of grazing animals (i.e., changes in soil properties and vegetation cover) and the off-site impacts of grazing animals (i.e., the impact of these changes on hydrology and water quality in surface waters), as these relationships have, in the past, only been alluded to by authors. However, there exists relatively little research evidence to support and quantify these relationships, thus herein we describe data required to address the lack of understanding of the role of grazing animals on grasslands. Finally, the last section of this chapter considers the land management and remediation options available for the reduction of the impacts of intensive livestock farming.

The impacts of grazing animals on the quality of soils, vegetation and surface waters in intensively managed grasslands

Spatial variability of soil total nitrogen and soil total phosphorus under different land uses in a small watershed on the Loess Plateau, China

Improvements of nitrogen use efficiency (NUE) may be achieved through the use of sensing tools for N status determination Leaf and canopy chlorophyll, as well as leaf polyphenolics concentrations, are characteristics strongly affected by N availability that are often used as a surrogate to direct plant N status estimation Approaches with near-term operational sensors, handheld and tractor-mounted, for proximal remote measurements are considered in this review However, the information provided by these tools is unfortunately biased by factors other than N To overcome this obstacle, normalization procedures such as the well-fertilized reference plot, the no-N reference plot, and relative yield are often used Methods to establish useful relationships between sensor readings and optimal N rates, such as critical NSI (nitrogen sufficiency index), INSEY (in-season estimated yield), and the relationship between chlorophyll meter readings, grain yield, and sensor-determined CI (chlorophyll index) are also reviewed In a few cases, algorithms for translating readings into actual N fertilizer recommendation have been developed, but their value still seems limited to conditions similar to the ones where the research was conducted Near-term operational sensing can benefit from improvements in sensor operational characteristics (size and shape of footprint, positioning) or the choice of light wavebands more suitable for specific conditions (ie, genotype, growth stage, or crop density) However, one important limitation to their widespread use is the availability of algorithms that would be reliable in a variety of soil and weather conditions

Strategies to Make Use of Plant Sensors-Based Diagnostic Information for Nitrogen Recommendations

https://core.ac.uk/download/pdf/140546.pdf

A systematic representation of crop rotations

A measure of soil P status in agricultural soils is generally required for assisting with prediction of potential P loss from agricultural catchments and assessing risk for water quality. The objectives of this paper are twofold: (i) investigating the soil P status, distribution, and variability, both spatially and with soil depth, of two different first-order catchments; and (ii) determining variation in soil P concentration in relation to catchment topography (quantified as the "topographic index") and critical source areas (CSAs). The soil P measurements showed large spatial variability, not only between fields and land uses, but also within individual fields and in part was thought to be strongly influenced by areas where cattle tended to congregate and areas where manure was most commonly spread. Topographic index alone was not related to the distribution of soil P, and does not seem to provide an adequate indicator for CSAs in the study catchments. However, CSAs may be used in conjunction with soil P data for help in determining a more "effective" catchment soil P status. The difficulties in defining CSAs a priori, particularly for modeling and prediction purposes, however, suggest that other more "integrated" measures of catchment soil P status, such as baseflow P concentrations or streambed sediment P concentrations, might be more useful. Since observed soil P distribution is variable and is also difficult to relate to nationally available soil P data, any assessment of soil P status for determining risk of P loss is uncertain and problematic, given other catchment physicochemical characteristics and the sampling strategy employed.

G. A. Wood

Papers

A systematic representation of crop rotations

Spatial variability of soil phosphorus in relation to the topographic index and critical source areas: sampling for assessing risk to water quality.

Soil Factors and their Influence on Within-field Crop Variability, Part II: Spatial Analysis and Determination of Management Zones

An Economic Analysis of the Potential for Precision Farming in UK Cereal Production

Precision Farming of Cereal Crops: a Review of a Six Year Experiment to develop Management Guidelines