Precision Agriculture for Grain Production Systems

Brett Whelan, James Taylor

• Publisher: CSIRO PUBLISHING
• Year: 2013
• ISBN:
  9780643107496
• Topic: Technology & Engineering
• Subtopic: Agronomy
• Index: Technology & Engineering

Precision Agriculture (PA) is an approach to managing the variability in production agriculture in a more economic and environmentally efficient manner. It has been pioneered as a management tool in the grains industry, and while its development and uptake continues to grow amongst grain farmers worldwide, a broad range of other cropping industries have embraced the concept. This book explains general PA theory, identifies and describes essential tools and techniques, and includes practical examples from the grains industry.

Readers will gain an understanding of the magnitude, spatial scale and seasonality of measurable variability in soil attributes, plant growth and environmental conditions. They will be introduced to the role of sensing systems in measuring crop, soil and environment variability, and discover how this variability may have a significant impact on crop production systems. Precision Agriculture for Grain Production Systems will empower crop and soil science students, agronomy and agricultural engineering students, as well as agronomic advisors and farmers to critically analyse the impact of observed variation in resources on crop production and management decisions.

1

Introduction to Precision Agriculture

Precision Agriculture (PA) is a now a term used throughout agricultural systems worldwide, but what is meant by Precision Agriculture? This introductory chapter provides a background to the principal philosophy and goals of a PA management strategy, the evolution of PA and some of the steps required to adopt PA in grain cropping systems. It provides a stepping-stone to subsequent chapters that will investigate the theories, technologies and methodologies behind the adoption of PA within grain production systems.

Defining PA

Many definitions of PA exist and many people have different ideas of what PA should encompass. Here, we have selected two definitions to illustrate the concept of PA in general and specifically its application to broadacre cropping industries. The first definition comes from the US House of Representatives (US House of Representatives 1997).

Precision Agriculture

An integrated information- and production-based farming system that is designed to increase long-term, site-specific and whole-farm production efficiency, productivity and profitability while minimising unintended impacts on wildlife and the environment.

The key to this definition is that it identifies PA as a ‘whole-farm’ management strategy, not just a strategy for individual fields. It uses information technology to improve production management and minimise environmental impact. This definition refers to the farming system that in modern agriculture may include the supply chain from the farm gate to the consumer; it also distinguishes between agriculture and agronomy. While the PA philosophy has been expounded primarily in cropping industries, it is important to remember that PA can relate to any agricultural production system. These may involve animal industries, horticulture, viticulture, fisheries and forestry; in many cases PA techniques are being implemented without being identified as such. For example, in a dairy enterprise the tailoring of feed allocations to individual cows may be based on yield and the stage of their lactation. The focus in this book is the implementation of PA in grain production.

The second definition narrows the PA philosophy of timely management of variation down to its implementation in cropping systems.

Site-specific crop management (SSCM)

A form of PA whereby decisions on resource application and agronomic practices are improved to better match soil and crop requirements as they vary in the field.

This definition encompasses the idea that PA is an evolving management strategy. The focus here is on decision-making with regard to resource use and not necessarily to the adoption of information technology on farm, although many new technologies will aid improved decision-making. The decisions can be in regard to changes across a field at a certain time in the season, or to changes through a season or seasons. The inference is that better decision-making will provide a wide range of benefits – economic, environmental and social – that may or may not be known or measurable at present (Figure 1.1).

From a grain production perspective, this definition provides a goal regardless of a grower’s current adoption of PA or proposed entry level into PA. Expanding this goal, SSCM can be considered as applying information at the site-specific level, with grower knowledge, to achieve the objectives of:

• optimising production efficiency;
• optimising quality;
• minimising environmental impact;
• minimising risk.

Objectives of SSCM

The success of an SSCM strategy will depend on how each or all of the above objectives are met.

Figure 1.1: The economic–environmental foundation for an SSCM to minimise operational and societal risk.

Optimising production efficiency

In general, the aim of SSCM is to optimise returns across a field. Rarely do fields have uniform yield potential, so identifying any variation in yield potential may offer the possibility of optimising yield production by varying management within fields. The initial emphasis should be on optimising the agronomic response to the manageable input that has the greatest impact on production and costs. In the absence of any clear environmental benefits, this will be achieved by differentially applying inputs up to the point that the marginal return (MR) = marginal cost (MC) at each site or in each region of the field that has been identified as having a different yield potential.

Optimising quality

In general, production efficiency is measured in terms of a yield (quantity) response, mainly because yield and biomass sensors are the most reliable and commonplace sensors. In the past few years the first attempts to commercialise grain quality sensors have been made and on-the-go grain protein/oil sensors are now commercially available. The ability to site-specifically collect grain quality data will allow growers to consider production efficiency from the perspective of yield or quality, or a yield–quality interaction. Many inputs will impact on quality as well as quantity. In production systems where quality premiums exist this may alter the amount of input required to optimise profitability and agronomic response.

In some product markets, where strong quality premiums/penalties are applied, a uniform approach to quality properties may be optimal. The quality of some agricultural commodities is greatly increased by decreasing the variability in production, e.g. malting barley. If quality premiums more than offset yield loss then growers may prefer to vary inputs to achieve uniform production quality (minimise variability in quality), rather than optimise yield productivity.

Minimising environmental impact

From an environmental point of view, more precise treatments may offer the prospect of reducing the environmental risk associated with uniform/blanket field treatments and provide the ability to work with the natural diversity in cropping systems.

If management decisions are tailoring inputs to meet production needs, then by default there must be a decrease in the net loss of any applied input to the environment. This is not to say that there is no actual or potential environmental damage associated with the production system; however, the risk of environmental damage is reduced.

By recording the amount and location of any input application, producers have physical evidence to contest any claims regarding negligent management. They can also provide information on ‘considerate’ practices, to gain market advantage. A by-product of improved information collection and flow is a general improvement in producers’ understanding of the production system and the potential implications of different management options.

Apart from avoiding litigation or chasing product segmentation into markets, there is currently little regulatory incentive for growers to capture and use information on the environmental footprint of their production in Australia. Other countries, particularly within the European Union (EU), are financially encouraging producers to collect and use this information by linking environmental issues to subsidy payments. Should such eco-service payments be introduced in Australia, the value of PA could be further enhanced.

Minimising risk

Risk management is a common practice for most farmers and can be considered from two points of view – income and environment. In a production system, farmers often practise risk management by erring on the side of extra inputs while the unit cost of a particular input is deemed ‘low’. Thus, a farmer may apply an extra agrochemical spray, add extra fertiliser, buy more machinery or hire extra labour to ensure that the produce is grown/harvested/sold on time, thereby guaranteeing a return.

Generally, minimising income risk is seen as more important than minimising environmental risk. SSCM attempts to offer a risk management solution that may allow both positions to be considered. This improved management strategy depends on a better understanding of the environment–crop interaction and a more detailed use of emerging and existing information technologies (e.g. short-and long-term weather predictions and agro-economic modelling).

The more that is known about a production system, the faster a producer can adapt to changes. Many inputs will impact on quality as well as quantity. In production systems where quality premiums exist, this may alter the amount of input required to optimise profitability and agronomic response. More information on the level of production quality may also permit a grower to better target products within the supply chain and minimise or better manage off-farm risks.

Implementation of SSCM

The SSCM cycle can be described with five key nodes (Figure 1.2). A brief introduction is provided here; further details are covered in later chapters. It is important to remember that SSCM is a continuous management strategy. Initially, some form of monitoring and data analysis are needed to make a decision. However, it is equally important to continue to monitor and analyse the effect of that decision, and feed that information into subsequent management decisions.

Geo-referencing

The ability to geo-reference data, i.e. link it to a specific location on the Earth’s surface, is the truly enabling technology of SSCM in its present form. Global navigation satellite systems (GNSS), of which the global positioning system (GPS) is the most widely used at present, are now common on many farms. Receivers, and the systems in which they are used, range in accuracy from ±10–20 m to ±2–3 cm and in price from A$200 to A$40 000. Applications include crop and soil monitoring, yield mapping and vehicle autosteer systems. The technology continues to improve and the price of receivers continues to decrease.

Figure 1.2: The SSCM cycle, showing that spatial referencing is the enabling technology that drives the other parts of the cycle.

The ability to link a location to an action or data allows producers to map and visually display farm operations. This provides insights into production variability as well as inefficiencies in crop production and farm operations. Recently, the more accurate GNSS have become more common on-farm as growers embrace vehicle guidance and autosteer technologies. These permit vehicles to travel along repeatable paths with minimum overlap, reducing driver fatigue and permitting greater timeliness in operations.

Crop, soil and climate monitoring

Many sensors and monitors already exist for in situ and on-the-go measurement for a variety of crop, soil and climatic variables. These include yield sensors, biomass and crop response sensors (aerial and space-borne multi- and hyper-spectral cameras), radio or mobile phone networked weather stations, apparent soil electrical conductivity (EC_a) sensors and gamma-radiometric soil sensors, to name a few. The majority of SSCM research in Australia is directed at identifying how to use the output from these sensors to improve pr...