Precision Agriculture
Introduction and Background
Agricultural Evolution. From the beginning of cultivation, and the earliest
settlement of land for food production or farming, it is easy to imagine that land
units were selected for cultivation based upon uniformity of the unit relative to
surrounding land. In fact, on the broadest scale, whole regions were identified for
cultivation based upon their relative uniform suitability compared to the
surrounding regions. Hence, the Central Great Plains region of the United States
was readily converted from prairie grassland to cultivated cropland. Forested
areas, rock outcrops and small mountainous areas were obvious exceptions.
Within this Great Plains region, pioneer farmers laid-out fields based upon the
economies of scale, which were severely restricted by their limited resources, and
demands of diversified agricultural systems. Early farms were designed to be
self-sufficient and included fields to provide feed, food, and cash crops. The
Homestead Act provided the settlers with 160 acres to develop within this self
imposed system or framework. Consequently, field sizes were often much
smaller (10 to 40 acres) than is common today (80 to 160 acres).
By
comparison, the average farm size in Oklahoma today is about 480 acres, and
the average size wheat field in Northcentral Oklahoma is 80 acres.
Field and farm size increases have paralleled advances in agricultural
technology and rural standards of living. Increases in the size of field equipment
naturally led to a desire for, and increase in, the size of fields. This in turn led to
an increase in the amount of land a farmer could manage. As a result, fields that
were initially small and whose boundaries were determined by a perceived
uniformity in characteristics like slope, texture, and productivity potential, and
which were used for the production of different crops (e.g. pasture, perennial
legumes like alfalfa, annual row crops like corn, cotton, soybeans and sorghum;
solid seeded annuals like wheat) were combined into larger fields that could most
efficiently be managed as larger, independent units.
Inherent Variability. The manner in which native land was developed for
agriculture was related to inherent variability among farms in communities and
among fields within farms. As example, some land was selected for cultivation
because it was level and free of rock outcrops and trees. Other land may have
been selected for livestock pasture (or ranching) because it was not level, was
partly forested, had rock outcrops or could provide water for livestock. These
differences still exist today.
Even the casual observer can easily distinguish gross natural or inherent
variability in the landscape. For example, the Stillwater surroundings are
dominated by forested uplands and a few cultivated creek-bottoms. The Enid,
Oklahoma area is primarily made up of flat, level cultivated land. On a finer scale,
or resolution, natural variability has been delineated by identifying and mapping
different soils (US Soil Survey). In the normal process of soil surveying, units as
small as five acres may be recognized. Consequently, within the average field of
80 acres several different soil series may exist. It is important from the standpoint
of Precision Agriculture to understand that soil surveys that recognize different
soils based on morphological features do not necessarily identify soils with
different production capacities or that have different crop production input needs.
Also, lines on soil survey maps that delineate different soils should not be
expected to reliably separate soils that are uniquely different, even in
morphological differences, because actual inspection of soil morphological
features was not performed on every five acres. Soil scientists delineated soils
based upon perceived differences associated with observations of changing
vegetation, slope, drainage patterns, etc., and their understanding of the local
influence of the soil forming factors (vegetation, parent material, slope, climate,
time). Nevertheless, some of these morphological differences among soils do
strongly impact crop production and the crop response to production inputs. Most
noteworthy are soil texture, soil depth, soil organic matter content and slope.
Acquired Variability. Additional field variability results from how fields or
soils within fields have been managed over time. An obvious example of this is
found as a result of farm consolidation and increasing field size over time. In the
early years of converting native land to cultivated land, field size was much
smaller than today. As farm and field size increased, several small fields that
had different management histories were consolidated into one larger field. In
extreme cases old abandoned farmsteads (buildings, trees, livestock feeding
pens, etc.) were removed or destroyed and the area incorporated into an existing
field. The new field acquired new variability, usually in excess of existing natural
or inherent variability. Another example of acquired variability is related to
terracing and its influence on field management. Terraces on fields with extreme
slopes may be too close to allow uniform treatment of the field, especially as it
relates to application of fertilizers or ag chemicals by large commercial applicators
(60-foot wide application). An example of soil test variability for a 20-acre field in
Noble County is illustrated below.
Terrace # 6
Terrace # 5
Terrace # 4
Terrace # 2
Terrace # 1
Bottom
pH = 4.9
BI = 6.6
N = 10
P = 93
K = 435
pH = 5.2
BI = 7.0
N = 13
P = 54
K = 354
Extra Lime , no P
pH = 5.3
BI = 6.9
N = 10
P = 44
K = 415
Terrace # 3
BAD
SPOT
pH = 5.7 7.3
BI = 6.9 -N = 20
67
P = 23
22
K = 397 343
Extra P2O5
pH = 5.4
BI = 6.8
N = 20
P = 31
K = 522
pH = 5.5
BI = 6.7
N = 12
P = 32
K = 423
FIELD
AVERAGE
pH = 5.2
BI = 6.8
N = 14
P = 49
K = 408
pH = 4.6
BI = 6.8
N = 16
P = 65
K = 310
FIELD
RANGE
pH = 4.6-5.7
BI = 6.6-7.0
N = 10-20
P = 23-93
K = 310-522
Extra
Lime
A strict interpretation of the average soil test values would result in recommending
1.2 ton/acre of effective calcium carbonate equivalent (ECCE) lime, 61 lb N/acre
(35 bushel yield goal for wheat grain, where the requirement is 2 lb N/bushel yield
goal), 13 lb P2O5/acre, and no K2O. However, if terraces were treated individually
according to soil tests, lime would range from 1.9 to 0.7 tons ECCE lime/acre, N
from 50 to 60 lb/acre, P2O5 from 0 to 37 lb/acre, and K2O would not be required
for any terraces.
Conventional Treatment of Variability.
As we consider Precision
Agriculture, and everything it involves and implies, we might benefit by first
becoming familiar with how variability is currently being treated in production
agriculture. Most production inputs are applied at a constant rate over entire
fields. Field size (average for wheat 80 acres) is strongly influenced by farm size
(Waggoner Ranch in Texas had a 7,500 acre wheat field) and field shape by legal
survey of land (sections, quarters, etc.) and physical features (rivers, roads, etc.).
Within field variability is generally not measured or treated in commercial
agriculture today. In the case of soil pH and nutrient management, where soil
testing is used, farmers are advised to collect 15 random samples from the field
and mix them in a bucket to obtain a composite for the field. This gives them a
reliable estimate of the mean, but no indication of the variability in the field.
Extremes in variability could result in some of the field receiving excessive inputs
and some of the field not receiving adequate inputs.
Perhaps the greatest failure in treating variability today is not failure to
recognize it within fields, but failure to recognize it among fields and failure to
even recognize it at all, at the field level. Examples of this are found in the results
of the OSU “Free Wheat” soil test for the summer of 1996, where it was found
that for participants in Garfield County (Enid) 67 % of the farmers that sampled
five fields fertilized all five the same and the average field size was 135 acres!
Forty two percent of the farmers tested their fields less frequently than every three
years, with four percent indicating they had never tested their wheat fields. The
following table shows an example of a farmer who had not tested fields for at
least 15 years. All but one field had acquired adequate levels of available N and
P from the standard practice of treating all fields with the same fertilizer input
each year. These same fields had apparently acquired a lime deficiency (soil pH
below 5.5) sufficient to cause yield reduction from aluminum toxicity. Although
great variability existed among the five fields that totaled 349 acres, all fields had
been treated the same. Farmers who soil tested on a regular basis were similarly
found to treat all fields the same, even when large differences were found in the
results among fields.
Garfield Co. Farmer’s Use of Soil Testing and Fertilization
Soil Test Results
N
P
Sur
Sub
86*
1981
35
100
46
4.5
24
54 106
118*
1981
25
100
46
4.9
53 108
88
30*
1989
34
100
46
5.1
44
43
75
65*
26
100
46
4.4
115 118 159
50
1981
29
100
46
5.5
0
70
44
*Savings from no fertilizer to four fields = 299 acres X $24.50/acre, = $7,325
Acres
Previous
Soil Test
Grain
Yield
Normal Fertilization
N
P2O5
K2O
pH
K
445
411
377
752
551
Should every managed production input for a field be considered at variable
levels? The answer to this question, at least in part, depends upon whether or
not there is a high probability of increasing the production profits from applying an
input to portions of the field at a different rate. If areas of the field can be
identified and marked for different rates, those rates can be applied to areas of
only a fraction of an acre without additional cost to the farmer. This was being
done on an estimated 10 % of the acreage in 1999 for fertilizer and lime
application and 20 % of the acreage for herbicides (personal communication,
Clyde Coop, Medford, OK). However, the question still remains as to the basis
for determining different input rates or levels. In the case of lime and fertilizer, it
is not obvious that a reliable soil test is the basis for identifying different rates.