Soil compaction is one of the major problems facing modern agriculture. Overuse of machinery, intensive cropping, short crop rotations, intensive grazing and inappropriate soil management leads to compaction. Soil compaction occurs in a wide range of soils and climates. It is exacerbated by low soil organic matter content and use of tillage or grazing at high soil moisture content. Soil compaction increases soil strength and decreases soil physical fertility through decreasing storage and supply of water and nutrients, which leads to additional fertiliser requirement and increasing production cost. A detrimental sequence then occurs of reduced plant growth leading to lower inputs of fresh organic matter to the soil, reduced nutrient recycling and mineralisation, reduced activities of micro-organisms, and increased wear and tear on cultivation machinery. This paper reviews the work related to soil compaction, concentrating on research that has been published in the last 15 years. We discuss the nature and causes of soil compaction and the possible solutions suggested in the literature. Several approaches have been suggested to address the soil compaction problem, which should be applied according to the soil, environment and farming system. The following practical techniques have emerged on how to avoid, delay or prevent soil compaction: (a) reducing pressure on soil either by decreasing axle load and/or increasing the contact area of wheels with the soil; (b) working soil and allowing grazing at optimal soil moisture; (c) reducing the number of passes by farm machinery and the intensity and frequency of grazing; (d) confining traffic to certain areas of the field (controlled traffic); (e) increasing soil organic matter through retention of crop and pasture residues; (f) removing soil compaction by deep ripping in the presence of an aggregating agent; (g) crop rotations that include plants with deep, strong taproots; (h) maintenance of an appropriate base saturation ratio and complete nutrition to meet crop requirements to help the soil/crop system to resist harmful external stresses.

Soil compaction in cropping systems: A review of the nature, causes and possible solutions

Precision agriculture—a worldwide overview

https://www.tucson.ars.ag.gov/unit/Publications/PDFfiles/MSM_1948.pdf

Opportunities and limitations for image-based remote sensing in precision crop management

https://pubag.nal.usda.gov/download/6636/PDF

Apparent soil electrical conductivity measurements in agriculture

Precision Agriculture is advancing but not as fast as predicted 5 years ago. The development of proper decision-support systems for implementing precision decisions remains a major stumbling block to adoption. Other critical research issues are discussed, namely, insufficient recognition of temporal variation, lack of whole-farm focus, crop quality assessment methods, product tracking and environmental auditing. A generic research programme for precision agriculture is presented. A typology of agriculture countries is introduced and the potential of each type for precision agriculture discussed.

Future Directions of Precision Agriculture

Remotely sensed data has been identified as an important tool for precision crop management (PCM). The data has been used to assist in the identification of management zones, map crop nutrient status, and detect pest infestations. However, in many of the examples cited, the correlation between a multispectral signature and the variation of interest was limited to single factor experiments (i.e., only one factor was primarily responsible for the variability in crop condition). A water by nitrogen experiment was conducted during the 1999 cotton season near Phoenix, Arizona, where one objective was to test the ability of remotely sensed data to distinguish between water and nitrogen stress. Multispectral (visible, near infrared and thermal) data were collected using a prototype sensor mounted on a linear move irrigation system. Neutron probe data were used to quantify crop water status, and petiole samples were used to

/pdf/coincident-detection-of-crop-water-stress-nitrogen-status-27w587b6oq.pdf

Coincident detection of crop water stress, nitrogen status and canopy density using ground-based multispectral data.

Adoption of precision agriculture technologies by U.S. farmers.

Soil electrical conductivity variability.

Evaluating management zone technology and grid soil sampling for variable rate nitrogen application.

Of the various factors that affect crop yield, soil water-holding capacity is usually a significant contributor. Soil electrical conductivity (EC) measurements in non-saline soils are driven primarily by soil texture and soil moisture. Those same factors correlate highly to the soil’s water-holding capacity. Thus, an EC map can serve as a proxy for soil water-holding capacity, resulting in soil EC and yield maps that frequently exhibit similar spatial patterns. Numerous commercial EC mapping systems are being used in precision agriculture, and many of the maps generated by these units are being layered in a GIS with yield data in an attempt to explain yield variability. A common tool being employed in yield-EC analyses is bi-variate linear regression. While this analysis frequently explains a larger percentage of yield variability than is explained by other available layers of soil sample information, it ignores the more complex relationships between soil physical properties and yield. Moving to a non-linear curve-fit may improve the correlation co-efficient but rarely explains more than 50% of the yield variability within a field. This paper presents an analysis technique that sorts through the cloud of yield data points to establish a yield benchmark for each soil EC level. Further analysis generates maps that can be used to investigate areas that are performing below the benchmark.

R. H. Rust

Papers

Coincident detection of crop water stress, nitrogen status and canopy density using ground-based multispectral data.

Adoption of precision agriculture technologies by U.S. farmers.

Soil electrical conductivity variability.

Evaluating management zone technology and grid soil sampling for variable rate nitrogen application.

Using yield and soil electrical conductivity (EC) maps to derive crop production performance information.