Monitoring crop yields in precision farming

Many of the decisions made during the cultivation of page - x. crops are based on yield records. Data on the yield of a particular crop in a particular field allows the commodity producer to make more correct and informed decisions about the doses of fertilizer application, to draw conclusions about how efficient production is in this field.

Commodity producers, agronomists and researchers subdivide the factors affecting the yield into natural and anthropogenic (Table 3.1). It is rather difficult to rank the factors according to the degree of their influence on the yield, since they change from year to year. In addition to this, many of them interact with each other both in time and in space.

1. Causes affecting yield variability

For example, a change in the depth of the arable layer affects not only the water-holding capacity of the site, but also the content of nutrients available to the plant, soil aeration, root formation, etc. The presence of moisture, including both its excess and deficiency, significantly affects the yield of agricultural crops. cultures. Soil scientists and agronomists are well aware that the yield is proportional to the amount of water absorbed by the plant or evaporated. For example, a plant development model based on plant moisture absorption alone explains 69% of the variability in soybean yield in Iowa.

Field yield mapping has recently become a common practice among US commodity producers. Some fields already have a three- to five-year history presented in yield maps.

The value of cards depends on how correctly they are analyzed. The main goal of interpreting yield maps is to increase profitability by better understanding the natural and anthropogenic causes that determine yield variability within a single field. Obviously, the information presented on the map has a certain error that can be corrected. Errors must be separated from actual yield variability across the field for more accurate map interpretation. For successful interpretation of the maps, additional information about the field is involved. To effectively assess the impact of the entire set of factors on productivity, GIS is used to establish a relationship between productivity and other characteristics of the field.

On the basis of yield data, a commodity producer can judge the advantages or disadvantages of a particular technology for cultivating a given crop. By studying the yield variability within a single field (on elementary plots), a commodity producer can determine the causes that cause this and eliminate them.

The most convenient form of presenting information on yield variability is a yield map showing the yield in individual areas with a rigid reference to a specific coordinate system.

Over the past five years in North America, the number of grain harvest monitors installed on grain combines has increased from 100 to 25,000 units. Nearly half of them are connected to DGPS receivers for yield mapping. This equipment, together with a computer, printer and related software, allows the producer to produce color maps of yields that reflect the variability of the yield from one area to another. Commodity producers hope with the help of this information to uncover the secrets of yield variability within a single field, improve their production efficiency and increase their net profit. Although the cards have become available to many manufacturers, their interpretation is much more difficult than they and their consultants expected.

Many factors affecting yield are interdependent. The key to interpreting the maps is a deeper understanding of the causes that cause changes in the yield, and identifying those that are due to the actions of the commodity producer himself during the cultivation of the corresponding agricultural crop. culture. Yield mapping is only effective if the information is used to inform better decision making. Work is currently underway to automate the yield mapping process using the latest advances in electronics and global positioning. Despite the fact that the technology of yield mapping is being widely introduced into the life of commodity producers, many technical issues remain unresolved. So, for example, the determination of the yield and coordinates of the aggregate is associated with many random and systematic errors. Therefore, when drawing up a map, it is necessary to take measures to avoid errors. The software currently in use corrects the yield data before presenting it as a map. But even taking into account the existing errors in determining the yield of agricultural crops. crops on the map of productivity, it is possible to determine the reasons that caused the variability of productivity in the field (Figure 1).

Rice. 1. Yield map and its interpretation

More efficient use of the yield map can be achieved by combining yield information with other field information such as topography, nutrient distribution, etc.

To obtain the information necessary to build a yield map, a number of sensors are installed on the combine (Figure 2). The heart of the mapping system is a yield sensor that measures yield either directly by weighing or indirectly. Currently, there are many different sensors for determining the yield (Figures 3, 4, 5). To obtain reliable information about the yield, high-precision sensors are needed. However, even with an accurate estimate of the mass of grain entering the bunker, it is not always possible to accurately determine the yield. This is due to a number of reasons:

Change in grain flow geometry;

Violation of the characteristics of the sensor, for example, due to changes in ambient temperature or vibration of the combine;

Change in grain moisture or density;

Clogging of the grain flow with various inclusions.

Rice. 2. The main elements of the yield monitor installed on grain combines

In this regard, the error in determining the yield by existing yield monitors is 3-8%.

Rice. 3. Claas yield weight sensor

Rice. 4. Radiation yield measurement sensor

Rice. 5. Volumetric yield sensor RDS Ceres

Bibliography

1. Knighton R. 1997. SMILEY: Remote Sensing Data-Mining Tool. Ver. 1.0. North Dakota St. Univ. http://smiley.cs.ndsu.nodak.edu/cgi-bin/smiley.cgi.

3. Lamb J. A., Dowdy R. H., Anderson J. L. and Rehm G. W. 1997. Spatial and Temporal Stability of Corn Grain Yields. J Prod. Agric., 10, pp. 351-414.

3 Mangold G. 1997. News Update from @gInnovator Online: Survey of yield monitor use in North America.

G. I. Lichman, Doctor of Technical Sciences, Head. lab. (GNU WIM)

A.I. Belenkov D. S.-H. n., professor, RGAU-MSHA named after K.A. Timiryazev)