Impediments to spatially-variable field operations

Computers, and electronics in agriculture Computers and Electronics in Agriculture 14 (1996) 249-253

Short Commuication

Impediments to spatially-variable field operations John K. Schueller Mechanical

Engineering

Department,

University

of Florida,

Gainesville,

FL 32611,

USA

Accepted 29 September 1995

Abstract Although the technology for spatially-variable field operations has advanced greatly, there are concerns which must be addressed for those operations to reach their full potential. These concerns include weather effects, substandard farm management, operator time demands, poor agronomic knowledge, and machine accuracy. Keywords: Spatially variable;

Field operation;

Global Positioning

System (GPS)

1. Introduction

This special issue documents many of the recent technical achievements in spatially-variable field operations. Combined with the earlier related issue [Vol. 11(l)] of this journal dealing with the use of the Global Positioning System (GE) in agriculture, it significantly advances the knowledge of the state-of-the-art of field operations. Spatially-variable field operations are unquestionably the wave of the future. Both researchers and farmers are invariably enthused when they understand the concept. The attractiveness of the sophisticated technology may account for some of its popularity. More of that popularity, however, is due to the perception that these technologies are compatible with the experienced holistic perceptions of contemporary and future crop production. As a consequence, there has been an explosion in related research and commercial developments. This explosion has been documented in the technical literature and widely publicized in the popular agricultural press. The enthusiasm has generally not been as widely shared by administrators in the governmental, university, and corporate communities. That so much has been achieved is certainly more attributable to the dedication of the enthusiasts and the soundness of the concepts, rather than the amount of support they have achifeved from the agricultural infrastructure. More effort needs to be expended in educating 0168-1699/96/$15.00 0 1996 Elsevier Science B.V. All rights reserved. SSDI 0168-1699(95)00051-8

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the decisionmakers about the potential of these technologies, their inevitable integration into normal agricultural practice, and their research and development needs. Otherwise, the period of enthusiasts needing to consciously publicize the technology is past. In fact, those who are committed to the technology may need to articulate the drawbacks and problems with tlhese technologies to avoid the dis,appointments generated by unrealistic expectations. Applying inputs and performing field operations in a spatially-variable manner is an obvious improvement in crop production. It makes sense from both economic and environmental perspectives. IIowever, it may be useful to review some of the limitations in order to retain a balanced perspective. 2. Weather effects

It must be remembered that the weather is almost always the overriding factor in crop production. The time history of rainfall and temperature will affect the production more than anything which can be controlled in a spatially-varia.ble manner. This makes the determination of the best control strategy difficult. The weather confounds the data. The yield maps and soil maps are therefore functions of many independent variables, including the many weather variables. Ultimately, this problem does not invalidate the desirability of spatially-variable field operations, but the problem may reduce the value of spatially-variable operations and make it more difficult to determine the optimum strategies. Little work has been done to determine optimum strategies while properly considering the stochastic nature of future weather probabilities and1 the autoregressive nature of crop growing conditions. Such work is needed if spatially-variable operations are’ to be properly controlled. rop production

management

t must also be remembered that crop production management is often substandard. Good spatially-variable control cannot be achieved unless good farm management practices are already in place. For example, spatially-variable pesticide application depends upon adequate pest scouting and knowing what must be done if a particular level of pest infestation is found. The production operation probably needs to have adopted other modern technologies, such as integrated pest management (IPM) and maximum economic yield (MEY), for spatially-variable field operations to achieve their full potential. The application of computers in agriculture to enable spatially-variable field operations closely parallels the application of computers in manufacturing to enable computer-integrated manufacturing (Cl&I). Whereas CIM moved discrete parts manufacturing from large batch sizes to lot sizes of one, spatially-variable control will move agriculture from uniform fields to similarly accounting for variability. Early CIM implementations were often problematic due to immature technology and applying CIM to situations which were not under proper management control. Hopefully, those types of mistakes will be less common in the computerization of crop production.

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4. Time demands

Spatially-variable field operations occur during the farm operator’s busiest times. Planting and harvesting are critical times in crop production. Every hour that is lost at peak planting or harvesting time must be replaced with an hour at the end of that season. The delay results in substantial crop production loss. Informal stuidies conducted around 1980 by this author of admittedly resource scarce, but realistic, Indiana corn and soybean production scenarios showed linear programming shadow values of over U.S. $1000.00 per hour for labor and equipment availability during the peak planting season. It was also found that the modern grain combine is very efficient if losses due to field conditions are avoided. Field conditions generally worsen as the harvest season progresses. Time losses due to spatially-variable demands on the machinery or machinery operator cannot be tolerated at either planting or harvesting. The spatially-variable field operations are more tolerable if they occur during off-seasons. For example, the manipulation of soil and yield maps into fertilization setpoint maps can reasonably demand some of the farmer’s time during winter. 5. Agronomy

The equipment and computer technologies for spatially-variable field operations have greatly advanced beyond the level of corresponding agronomic knowledge. As the articles in this issue demonstrate, technologies are available to map soils and yields and to control field operations such ;as fertilizer and pesticide application. I-Iowever, the best algorithms to get from crop and soil maps to setpoint maps for particular crops and fields are unknown. These algorithms need to be developed, and despite the energetic leadership of some agronomists, many agricultural scientists have not recognized the impending changes necessary in their research and technology transfer paradigms. For example, crop varieties need to be optimally bred for specific conditions within fields rather than to be robust to a wide range of conditions. Another example would be fertilizer recommendations which account for local topography, such as hillside position. 6. Operation

accuracy

The actual accuracy of spatially-variable field operations has not been effectively addressed. Simulation modeling has shown that a liquid fertilizer applicator with poor dynamic response is no better than uniform application (Schueller and Wang, 1994). That is just an example of h.ow inaccuracies can negate the advantages of spatially-variable field operations. Another example is the delays and averaging (themselves dependent upon another set of variables) in harvesters while yield mapping (Searcy et al., 1989). Potential error sources include errors in the locator, map generator, control algorithm, machine controller, and in the machine’s lateral direction. The errors of these sources are relatively unknown, as is the integration of all the component errors into an overall error. The overall accuracy performance of

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to demonstrate

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7. Field characteristics

Every field situation is different. rJniform treatment of fields considered them as a single entity. Applying a geometric analogy, a field could be thought of as being a single point in a multi-dimensional field-descriptive space with certain dimensional attributes, such as average yield, general topography, average organic matter content, and average soil phosphorus content. For a given operation (e.g., phosphorus application), one decision would have to be made on that field, and in fact could be applied to other similar fields which were described by nearby points in that multi-dimensional descriptive space. With spatially-variable control, the single point is replaced by a multi-dimensional surface which is overlaid over the planar geographic co-ordinates of the field. In contemporary spatiallyvariable practice, the infinite points of the surface corresponding to all co-ordinate combinations are replaced with a finite number of points, each representing the average value of each cell or contour area within the field. The greater number of points and the absence of field averaging means that there will likely be more points where there is less available historic data or algorithms to guide the determination of the desired field operation adjustments. Each field will ha.ve a different collection of descriptive points, making general statements on the economic value of spatially-variable operations, and even the variability typically encountered, difficult. (This inability to quantify typical economic advantages is one of the contributors to the lack of support from agricultural’ administrators mentioned earlier.)

The previous sections discuss some of the problem areas often neglected in discussions of spatially-variable field operations. However, there are some opportunities or positive factors which are similarly forgotten. There are legal and political advantages to spatially-variable field operations. Currently commercial spatially-variable field operations are primarily justified in North America on an economic basis, but the true impetus for adoption of these operations may result from regulations. There may be additional regulatory limits on the application of pesticides and fertilizers to locations where they are ‘needed’. They would then likely be spatially applied. That scenario is not that different in general concept from the special use permits or (admittedly larger scale) geographic restrictions applied to some pesticides now. A milder form of regulation would be the generalization of the ‘bubble’ concept of environmental impacts, i.e., limiting the total amount of release from an entire industrial complex rather than for an individual process. For example, the farmer could be allotted a total amount of fertilizer for his farm. In such a resource-scarce environment, spatially-variable application would be particularly advantageous.

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The technologies of spatially-variable field operations have an aesthetic appeal to many farmers and researchers. The maps of yields and desired application rates are intuitive and reflect the perceptions of many farmers. In addition, spatially-variable operations produce a sense of control and management achievement. 9. Conclusion

Although the articles in this special issue document the great technological progress made recently, there are a number of concerns discussed in this article which must be addressed for spatially-variable field operations to achieve their great potential. References SchueIIer, 3.K. and Wang, M.W (1994) Spatially-variable fertilizer and pesticide and DGPS. Comput. Electron. Agric., 1 i( 1): 69-83. Searcy, S.W.. Schueller, J.K., Bae, Y.H., Borgelt, S.C. and Stout, B.A. (1989) vanable yield during grain combining. Trans. ASAE, 32(3): 826-829.

application Mapping

with

GPS

of spatially-