comparisons of uniform and variable rate nitrogen and phosphorus ...

COMPARISONS OF UNIFORM AND VARIABLE RATE NITROGEN AND PHOSPHORUS FERTILIZER APPLICATIONS FOR GRAIN SORGHUM C. Yang, J. H. Everitt, J. M. Bradford

ABSTRACT. Variable rate fertilizer application has the potential to improve fertilizer use efficiency, increase economic returns, and reduce environmental impacts. This study was designed to examine differences in yield and economic returns between uniform and variable rate fertilizer applications. During the 1997 and 1998 growing seasons, a variable rate applicator, capable of varying two liquid fertilizers simultaneously, was used to evaluate three fertility strategies: conventional uniform N, uniform N and P, and variable rate N and P. The three treatments were assigned in six blocks within three 14–ha grain sorghum fields (two blocks in each field) in a randomized complete block design. Thirty–six soil samples were taken in a staggered systematic grid from each field, and levels of soil nutrients were determined. Application rate maps for the variable rate N and P treatment were generated based on a fixed yield goal and site–specific soil N and P levels across the experimental plots, while application rates for the uniform N and P treatment were calculated from the same yield goal and average soil N and P levels for all three fields. Yield monitor data indicated that the variable rate treatment resulted in significantly higher yields than the uniform N and P treatment for both years (400 kg/ha higher in 1997 and 338 kg/ha higher in 1998). Moreover, coefficients of variation of yield monitor data for the variable rate treatment were smaller than those for the two uniform rate treatments. A simple economic analysis showed that the variable rate treatment had positive relative economic returns over the uniform N and P treatment ($27/ha in 1997 and $23/ha in 1998). However, if additional costs for soil sampling, equipment, and data analysis associated with variable rate application were considered, these returns would be much lower or even negative. These results showed that variable rate fertilization can increase yield, reduce yield variability, and improve economic returns. More experiments are needed to evaluate the long–term agronomic, economic, and environmental viability of variable rate technology in the Rio Grande Valley of south Texas Keywords. Grain sorghum, Precision agriculture, Profitability, Variable rate applicator, Yield monitor.

T

he use of precision farming techniques, such as grid soil sampling, yield monitoring, and variable rate application, has been steadily increasing over the last decade. Precision farming aims to increase farm profits and reduce environmental impacts by adjusting production inputs, such as fertilizers, pesticides, and seeds, to specific conditions within each area of a field. Characterizing spatial variability in growing conditions within fields is the first important step in precision farming. Many approaches have been used to identify spatial variability within a field, including grid soil sampling, yield monitoring, and remote sensing. Grid soil sampling is a commonly used method for assessing variability in soil fertility and provides the basis for variable rate fertilizer recommendations. However, cost and labor associated with intensive grid sampling make it necessary that other approaches, such as remote sensing, be jointly used (Ferguson et al., 1996). Combine–mounted yield monitors are widely used for monitoring and mapping spatial

Article was submitted for review in February 2000; approved for publication by the Power & Machinery Division of ASAE in October 2000. Mention of trade names or commercial products in this article is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. The authors are Chenghai Yang, ASAE Member Engineer, Agricultural Engineer; James H. Everitt, Range Scientist; and Joe M. Bradford, Supervisory Soil Scientist, USDA–ARS, Kika de la Garza Subtropical Agricultural Research Center, Weslaco, Texas. Corresponding author: Chenghai Yang, USDA–ARS, 2413 E. Highway 83, Weslaco, TX 78596; phone: 956–969–4824; fax: 956–969–4893; e–mail: [email protected]. usda.gov.

Vol. 44(2): 201–209

yield variability. Yield maps offer important information not only for variable rate applications, but also for whole–field investments in drainage, land leveling, and irrigation (Swinton and Lowenberg–DeBoer, 1998). Remote sensing has been used for many years to monitor crop growing conditions and forecast yields. More recently, remotely sensed imagery, especially airborne imagery from video and digital cameras, is becoming a valuable data source for precision farming. Yang and Anderson (1996, 1999) reported on the use of airborne digital videography for establishing within–field management zones for precision farming. One unique advantage of remote sensing over other approaches is that it allows early detection of potential crop problems, such as nutrient deficiencies, water stress, or pest infestations, while there is still a chance to correct the problem. Using spatial variability information to make nutrient application recommendations could lead to higher profits if the inputs are varied according to the conditions for any given area. Currently, site–specific management recommendations for fertilizers are based on the same fertilizer guides developed for whole–field management. These guides were developed by combining results from a number of fertilizer response studies over a wide range of physiographic areas and soil types into simplified recommendation equations (Kitchen et al., 1995). Current fertilizer recommendations were not developed for the purpose of precision farming, and there is a noteworthy lack of experimental data to support this use of the recommendations (Blackmer and White, 1998). Nevertheless, these recommendations and guides are still useful before new fertilizer recommendations are developed. Most recommendation equations require that yield

Transactions of the ASAE E 2001 American Society of Agricultural Engineers

201

expectations and existing soil nutrient levels be determined. Yield maps made from yield monitor data over several years can be used to determine yield potential maps for variable rate applications. However, because crop yields are greatly affected by factors such as annual weather variations, crop rotations, and infestations of weeds, insects and diseases, it may be difficult to obtain reliable yield potential maps. For this reason, many research studies of variable rate applications use a uniform yield goal (Ferguson et al., 1996). Once spatial variability has been identified and variable rate plans formulated, specialized equipment must be available to implement the plans. To do this, an applicator with variable rate control can be used to automatically change application rates as it travels across a field. Variable rate application equipment is available for a variety of substances including granular and liquid fertilizers, pesticides, seed, and irrigation water (Searcy, 1997). Public and private research projects have been conducted in the development of variable rate systems, and several companies are currently marketing variable rate application equipment (Clark and McGuckin, 1996). While a significant number of producers have embraced the site–specific management concept, only a relatively small percentage of producers have actually implemented it because reliable, user–friendly equipment is not yet readily available, and more importantly, they are uncertain if this technology will increase their profits. Indeed, evidence of profitability for precision farming has been mixed or missing (Swinton and Lowenberg–DeBoer, 1998). In the Rio Grande Valley of south Texas, many farmers are aware of the emerging farming techniques, but are reluctant to invest in the equipment required for variable rate application before potential benefits become evident. As part of the precision farming program with the USDA–ARS Kika de la Garza Subtropical Agricultural Research Center at Weslaco, Texas, this two–year study was designed to evaluate differences in grain sorghum yield and economic returns between uniform and variable rate fertilizer strategies using a variable rate fertilizer applicator.

METHODS DESCRIPTION OF A VARIABLE RATE FERTILIZER APPLICATOR A variable rate fertilizer applicator was assembled by adapting an Ag–Chem FALCON (Fertilizer Applicator Local Controls Operating Network) controller to an 8–row (0.965 m spacing) side–dressing fertilizer applicator. The applicator could regulate rates of two different liquid fertilizers simultaneously. A 1500–L tank and a 950–L tank were mounted on the applicator to carry fertilizers N32 (32–0–0) and 11–37–0, respectively. The FALCON control system included an industrial–duty computer equipped with controller software, a power and network breakout box, three network nodes (microprocessors that control motors, meters, valve switches, and sensors), a radar speed sensor, and two sets of hydraulic motor driven centrifugal pumps, servo valves, flow rate meters, and shutoff valves. The two pumps were driven by the hydraulic system of the tractor used to pull 202

the applicator. Flows of the two products from the FALCON system were evenly distributed to sixteen knives by two flow dividers. A NorthStar GPS receiver was used to provide location information with submeter accuracy for the applicator in 1997. The receiver was configured to receive real–time differential correction signals from a base station located at a distance of 35 km. In 1998, a Trimble AgGPS 132 receiver was used to deliver submeter position information with OmniSTAR satellite differential technology. Shortly before fertilizer applications for the 1997 and 1998 growing seasons, the control and delivery components of the applicator were tested and calibrated in the laboratory to make sure that accurate rates were delivered to the flow dividers and that flows were evenly distributed to the knives. Dynamic response tests were also performed to determine response time and error of the FALCON controller. Results showed that the control system had very good static and dynamic performance and that the controller could stabilize at desired rate within 1 to 2 s (Yang et al., 1998, 1999). GRID SOIL SAMPLING Three irrigated grain sorghum fields of approximately 14 ha each owned by Rio Farms, Inc., at Monte Alto, Texas (26°23i N, 97°58i W) were selected for this study. Grid soil sampling was used to determine existing soil nutrient levels for fertilizer recommendations. Thirty–six soil sampling sites were determined from each field in a staggered systematic grid with a grid spacing of 60 m, as shown in figure 1. This sampling scheme reduced the possibility for bias in both the row and column directions in the presence of repeating patterns, such as tillage and fertilizer application. The sampling points were then located with a Trimble GPS Pathfinder Pro XRS submeter differential system and flagged across the three fields. Soil samples were taken to a depth of 30 cm shortly before planting for both the 1997 and 1998 growing seasons. Each sample consisted of 8–10 soil cores randomly collected within a 3–m radius of the sampling point. These samples were analyzed by a local soil and plant analysis laboratory to determine soil texture, organic matter (OM), pH, electrical conductivity (EC), NO3–N, P2O5, and H2O soluble and CO2 extracts of K, Na, Ca, and Mg. The univariate statistics of these soil variables were calculated for all the three fields. Correlation coefficients were calculated for all the variables between the two years. Maps for the soil properties and nutrients were generated using SURFER (Golden Software, Inc., 1997). EXPERIMENT DESIGN The experiment included three fertilizer application strategies. The three treatments were assigned in six blocks within the three fields (two blocks in each field) in a randomized complete block design, as shown in figure 1. The dimensions of the 18 individual experimental units were approximately 60 m by 385 m. Treatment 1 was a uniform N application at 100 kg/ha. This is close to what the farmer normally applies, though different rates or other fertilizers, such as P, are sometimes used. Treatment 2 included uniform applications of N and P, while treatment 3 included variable

TRANSACTIONS OF THE ASAE

West Field (Blocks 1 and 2) Middle Field (Blocks 3 and 4) East Field (Blocks 5 and 6)

Sampling Points Treatment 1: Uniform rate N Treatment 2: Uniform rate N and P Treatment 3: Variable rate N and P

N W

0

100

200

300

E

400 Meters S

Figure 1. Soil sampling points and layout of three fertilizer treatments in six blocks within an experimental area consisting of three grain sorghum fields in 1997 and 1998.

rate applications of N and P. There is no standard fertilizer guide for grain sorghum in south Texas, and available fertilizer recommendations vary. Based on available information and suggestions from local agronomists and farmers, N and P rates for treatments 2 and 3 were determined by the differences between total nutrient requirements and soil test levels divided by fertilizer use efficiencies. Total N and P requirements rely on expected yields for the fields. A uniform yield goal of 7000 kg/ha was used in 1997, while the yield goal was adjusted to 6000 kg/ha in 1998 because of significantly lower yield achieved in 1997. Total N and P needed to produce the expected yield were set at 180 kg/ha and 90 kg/ha, respectively, in 1997, compared to 160 kg/ha and 80 kg/ha in 1998. Use efficiency was considered to be 0.80 for N and 0.25 for P. The fertilizers used were N32 and 11–37–0. N32 contains 32% of N and its density is 1.324 kg/L, while 11–37–0 contains 11% of N and 37% of P2O5 and its density is 1.434 kg/L. Therefore, 1 L of N32 contains 0.424 kg of N, while 1 L of 11–37–0 contains 0.158 kg of N and 0.531 kg of P2O5. For treatment 1, only fertilizer N32 was needed and the rate for N32 was 236 L/ha (100/0.424). For the other two treatments, both fertilizers were needed. The rate for 11–37–0 was determined based on total P2O5 needed and soil existing P2O5, while the rate for N32 was calculated from total N needed, soil existing N, and the N contained in 11–37–0. The following formulas were used to calculate N32 and 11–37–0 rates for treatments 2 and 3: 11–37–0 needed (L/ha) = [(Total P2O5 needed – Soil P2O5)/0.25] / 0.531

Vol. 44(2): 201–209

(1)

N32 needed (L/ha) =[(Total N needed – Soil N)/0.8 – (11–37–0 needed × 0.158)] / 0.424

(2)

For treatment 2, soil N and P2O5 levels were the means determined from all the 108 samples. For treatment 3, soil N and P2O5 levels varied across the fields. The actual variable rate N32 and 11–37–0 maps for driving the FALCON controller were generated using the inverse distance method with SGIS software (Ag–Chem Equipment Co., Inc., 1997). These variable rate maps consisted of cells with a size of 1.5 × 1.5 m2. Each cell was assigned a rate level for each product to indicate how much product needed to be applied to that area of the field. There were 200 different rate levels equally spaced between the low and high rates for each product to offer a smooth continuous gradient from one cell to another. Since the controller could not regulate very low rates well, cells that were supposed to receive a rate of less than 47 L/ha (5 gal/ac) for a fertilizer received none of that product, while those that were supposed to receive a rate between 47 and 94 L/ha received 94 L/ha. During field application, only the plots for the variable rate treatment received the rates as recommended by the variable rate maps. The rest of the plots received either a uniform N32 rate for treatment 1 or uniform N32 and 11–37–0 rates for treatment 2. FIELD APPLICATION AND YIELD MONITORING The applicator was used to apply predetermined uniform and variable rates for N32 and 11–37–0 within each experimental plot across the three fields about six to seven weeks after planting in both 1997 and 1998. An AgLeader Yield Monitor 2000 system equipped with the same Trimble

203

AgGPS 132 receiver was used to collect yield and positional data during harvest of the experimental plots. The yield monitor recorded harvest area, grain weight, average moisture, and average yield for each plot. It also collected instantaneous yield and moisture data along with position information onto a memory card at one–second intervals.

RESULTS AND DISCUSSION SOIL NUTRIENT MAPS AND VARIABLE RATE RECOMMENDATION MAPS Table 1 summarizes univariate statistics of the soil properties and nutrients for the experimental area consisting of three fields in 1997 and 1998 and correlation coefficients for all the soil variables between the two years. Soil texture in these fields ranged from sandy with fast internal drainage and very low water holding capacity to loam with fair internal drainage and water holding capacity. Soil organic matter (OM) content (humus fraction) was very low with an overall mean of approximately 0.2%. Soil pH varied from acid to alkaline, though mostly slightly acid. Soil electrical conductivity (EC) levels were generally very low with only a few high values. Soil nutrient levels for N, P, K, Na, Ca, and Mg exhibited obvious variability within the three fields. As table 1 shows, mean levels of the soil properties were fairly consistent over the two years, while levels of a few nutrients changed somewhat. There existed significant correlations for all the soil variables between the two years. Correlation coefficients varied from 0.44 for N to 0.97 for EC, indicating that N had the least stable pattern between the two years, while EC had the most stable pattern over the years. Figures 2 and 3 show soil test N and P2O5 maps, respectively, for one of the three fields, referred to as the east field, in 1997 and 1998. These maps reveal spatial patterns of soil N and P2O5 for blocks 5 and 6 of the experimental area between the two years. Table 2 shows mean soil N and P2O5 levels within respective experimental plots for each of the

three treatments in 1997 and 1998. No significant differences existed in these two nutrients among the three treatments for either year. Table 3 shows mean soil N and P2O5 levels within respective experimental plots for each of the six blocks. Significant differences were found in these two nutrients among some of the blocks for both years, indicating blocking was useful and helped remove variations among the blocks. Figures 4 and 5 show fertilizer rate recommendation maps for N and P2O5, respectively, for the three treatments within the east field in 1997 and 1998. Since the fertilizer recommendations were based on a fixed yield goal, the patterns exhibited in the variable rate maps reflect those of the soil test maps: higher fertilizer rates correspond to lower soil levels. Areas where the soil P levels exceeded 90 kg/ha in 1997 and 80 kg/ha in 1998 received no P2O5, as shown by the unshaded areas in figure 5. Table 4 shows N and P2O5 fertilizer rates for the three treatments in 1997 and 1998. The N fertilizer rates for treatment 2 were almost the same as the average rates for treatment 3, while average P fertilizer rates for treatment 3 were higher than the P rates for treatment 2. This is because some of the soil P2O5 values were higher than the total P2O5 needed. These higher values increased the overall means of soil P levels for treatment 2, thus reducing the fertilizer P requirement for treatment 2, while the values higher than 90 kg/ha in 1997 and those higher than 80 kg/ha in 1998 did not help reduce the P fertilizer requirement for treatment 3. Since this study was conducted on the same area over a two–year period, the first year’s fertilizer treatments may have had some effects on the soil testing results and fertilizer recommendation maps for the second year. As shown in table 2, mean N levels within the experimental plots for treatments 2 and 3 in 1998 were not significantly higher than those for treatment 1, even though more N was applied to treatments 2 and 3. The same is true for P2O5; additional P fertilizer in 1997 did not significantly increase soil P2O5

Table 1. Univariate statistics of soil properties and nutrients within an experimental area consisting of three 14–ha fields in 1977 and 1998 and their correlation coefficients between the two years. 1997 (n = 108) Soil Variables

Range

Mean

1998 (n = 108) SD

Texture Sandy–loam – – OM (%) 0.10–0.45 0.20 0.06 pH 5.4–7.6 6.1 0.4 EC[b] (dS/m) 0.00–8.16 0.35 1.17 N (kg/ha) 17.9–98.6 37.0 12.4 P2O5 (kg/ha) 13.5–228.7 73.7 31.4 K–H2O[c] (mg/kg) 20–90 36 13 K–CO2[d] (mg/kg) 33–132 59 18 Na–H2O (mg/kg) 32–660 90 102 Na–CO2 (mg/kg) 57–758 218 107 Ca–H2O (mg/kg) 9–608 56 67 Ca–CO2 (mg/kg) 58–1404 146 144 Mg–H2O (mg/kg) 4–251 16 27 Mg–CO2 (mg/kg) 17–404 41 43 [a] Significant at the 0.01 level. [b] Electrical conductivity. [c] H O–soluble cations determined on an atomic absorption spectrophotometer. 2 [d] Extracted with CO (carbonic acid equivalent). 2

204

Range

Mean

SD

Correlation Coefficient

Sandy–loam 0.05–0.45 5.0–7.5 0.00–4.32 22.4–65.0 12.3–236.5 18–67 38–100 20–492 70–528 10–232 44–640 3–66 19–191

– 0.19 5.9 0.16 40.4 66.1 32 56 48 125 36 138 9 35

– 0.08 0.5 0.59 8.7 32.6 8 13 60 56 33 99 10 23

– 0.70[a] 0.84[a] 0.97[a] 0.44[a] 0.76[a] 0.65[a] 0.75[a] 0.94[a] 0.75[a] 0.70[a] 0.80[a] 0.80[a] 0.92[a]

TRANSACTIONS OF THE ASAE

2

3

2

1

3

2

3

1

1

kg/ha 3

2

1

65 60 55 50 45 40 35 30 25 20

East Field (1998)

East Field (1997)

15

Figure 2. Soil NO3–N maps based on grid sampling from one of the three fields for a variable rate application study in 1997 and 1998 (1–uniform rate N, 2–uniform rate N and P, and 3–variable rate N and P).

2

3

1

2 3

2

3

1

1

kg/ha 3

2

1

150 130 110 90 70 50 30

East Field (1997)

10

East Field (1998)

Figure 3. Soil P2O5 maps based on grid sampling from one of the three fields for a variable rate application study in 1997 and 1998 (1–uniform rate N, 2–uniform rate N and P, and 3–variable rate N and P). Table 2. Mean soil N and P2O5 levels (kg/ha) within respective experimental plots for each of the three treatments in 1997 and 1998.

Treatment

No. of Samples

1997 N

1998 P2O5

N

P2O5

1–uniform N 36 38.5a* 74.1a 39.2a 58.5a 2–uniform N&P 36 36.5a 74.4a 40.7a 72.7a 3–variable N&P 36 35.9a 72.7a 41.2a 67.2a * Means followed by different letters in the same column are significantly different at the 0.05 level based on Fisher’s least significant difference (LSD) tests.

levels in 1998. However, because the additional N and P fertilizers were not uniformly applied over the experimental area in 1997, these effects should be reflected in the soil N and P2O5 maps (figs. 2 and 3), and eventually in the fertilizer recommendation maps (figs. 4 and 5).

Vol. 44(2): 201–209

Table 3. Mean soil N and P2O5 levels (kg/ha) within respective experimental plots for each of the six blocks in 1997 and 1998.

Block

*

No. of Samples

1997 N

1998 P2O5

N

P2O5

1 18 40.2ab* 64.2b 41.6ab 70.0ab 2 18 42.5a 92.0a 39.6b 83.1a 3 18 29.8c 55.7b 33.1c 43.0c 4 18 38.0ab 53.4b 45.7a 55.1bc 5 18 38.1ab 86.8a 41.8ab 70.7ab 6 18 33.1bc 90.2a 40.6ab 74.9ab Means followed by different letters in the same column are significantly different at the 0.05 level based on Fisher’s least significant difference (LSD) tests.

YIELD DIFFERENCES BETWEEN UNIFORM AND VARIABLE RATE TREATMENTS Table 5 shows grain sorghum yields for the three fertilizer treatments among the six blocks, as well as yield means by

205

2

3

2

1

3

2

3

kg/ha

1

3

1

2

1

210 200 190

150

179

180

100 100

150 179

170

100

100

160 150 140 130 120 231321 E a st F i e ld ( 1 9 9 8 )

East Field (1998)

East Field (1997)

110 100

Figure 4. Uniform and variable rate N fertilizer recommendation maps for one of the three fields for a variable rate application study in 1997 and 1998 (1–uniform rate N, 2–uniform rate N and P, and 3–variable rate N and P).

2

3

1

2 3

2

3

1

1

3

kg/ha

2

1 200

55

160

0 55

65

0 120

0 65

0 80

40

East Field (1997)

52301321 E 550 a5 s t F i e l d (1 9 9 8 )

East Field (1998)

0

Figure 5. Uniform and variable rate P2O5 fertilizer recommendation maps for one of the three fields for a variable rate application study in 1997 and 1998 (1–uniform rate N, 2–uniform rate N and P, and 3–variable rate N and P). Table 4. N and P2O5 fertilizer rate recommendations (kg/ha) for three treatments in 1997 and 1998. 1997 Treatment

N

1–uniform N 2–uniform N and P 3–variable N and P

100.0 178.8 179.4[a] (115.2–202.6) [b] [a] Average of fertilizer rates for the variable N and P treatment. [b] Range of fertilizer rates for the variable N and P treatment.

block and by treatment for the 1997 and 1998 growing seasons. Grain yields for the variable rate treatment were the highest among the three treatments in 5 of the 6 blocks in both years. Mean yields for treatments 1, 2, and 3 in 1997 were 2378, 2313, and 2713 kg/ha, respectively, compared to 3961, 4404, and 4742 kg/ha for the three treatments in 1998. The overall average yield in 1998 was 4369 kg/ha, about 77% 206

1998 P2O5

N

P2O5

0.0 65.1 92.7 (0.0–306.0)

100.0 149.6 149.0 (124.3–170.6)

0.0 55.4 77.8 (0.0–270.5)

higher than the 1997 overall yield (2468 kg/ha). In 1997, the mean yields in none of the experimental plots achieved the expected yields, even though the yield in some areas of the fields was close to 6000 kg/ha. The extremely low yields in 1997 were mainly due to crop damage caused by excessive early season rainfall. In fact, shortly after the fields were planted in late February, the precipitation across the study TRANSACTIONS OF THE ASAE

Table 5. Grain sorghum yields (kg/ha) for three fertilizer treatments among six blocks in 1997 and 1998 as well as mean yields by treatment and by block. Block 1

2

3

4

5

6

Mean by Treatment

1–uniform N 2–uniform N&P 3–variable N&P

2071 2210 3006

1869 1857 2084

2959 2324 3423

2185 2286 1894

2614 2349 2741

2567 2854 3132

2378b [a] 2313b 2713a

Mean by block

2429

1937

2902

2122

2568

2851

2468

1–uniform N 2–uniform N&P 3–variable N&P

3699 4663 4797

4278 3886 4674

3856 4057 4495

3226 4524 4498

4428 4551 5145

4282 4741 4842

3961c 4404b 4742a

Mean by block

4386

4279

4136

4083

4708

4622

4369

Year

Treatment

1997

1998

[a] Means followed by different letters in the same year are significantly different at the 0.10 level based on Fisher’s least significant difference (LSD) tests.

area for the following month was 261 mm, compared to 55 mm of normal rainfall for the month. The extremely heavy rainfall resulted in crop plant damage and stand loss. The reduced plant density allowed weeds to grow easily across the experimental area, further reducing grain yield. In 1998, although the yield in many areas exceeded the expected yield (6000 kg/ha) and the highest yield was over 9000 kg/ha, the mean yields in none of the plots, including variable rate plots, reached the yield goal, indicating that factors other than N and P also affected yield. Table 6 shows analyses of variance for the three fertilizer treatments in 1997 and 1998. The overall F values were significant at the 0.02 level for both years, indicating that the models as a whole accounted for a significant portion of the variability in grain sorghum yields. In fact, about 75% of the variability in 1997 and 74% of the variability in 1998 were accounted for by block and treatment effects. The treatment effect was significant at the 0.10 level in 1997 and at the 0.01 level in 1998, while the block effect was significant at the 0.02 level in 1997 and at the 0.17 level in 1998. LSD multiple comparison tests revealed that the variable rate treatment resulted in significantly higher average grain yields than the two uniform rate treatments in both years (table 5). In 1997, no significant difference was found between the two uniform treatments, even though more N and P fertilizers were applied for treatment 2. As shown in table 5, grain yields for treatment 2 were lower than those for treatment 1 in 3 of the 6 blocks. The low yields for treatment 2 in blocks 3 and 5 were mainly due to more severe weed infestations. In 1998, the mean yield from uniform N

and P rate treatment was significantly higher than that from the uniform N rate treatment. These results from both years indicate that variable rate application could enhance fertilizer use efficiency and increase grain yield. EFFECT OF VARIABLE RATE APPLICATION ON YIELD VARIABILITY Figure 6 shows grain sorghum yield maps for the east field in 1997 and 1998. Yield varied from 0 to about 6000 kg/ha in 1997 and from 0 to about 9000 kg/ha in 1998. It is difficult to see the yield differences among the three treatments because the differences were generally small, and large yield variability existed in all the experimental plots. However, the obviously lower yield for treatment 1 in the plot located between the two plots for treatment 3 can be easily seen in the 1998 yield map. It is even more difficult to visually evaluate yield uniformity from the yield maps among the three treatments. Therefore, the individual yield monitor data points were grouped by experimental plots and coefficients of variation of yield were calculated, as shown in table 7. The variable rate treatment had the lowest coefficients of variation in 4 of the 6 blocks in 1997 and in 5 of the 6 blocks in 1998. The overall coefficients of variation for treatments 1, 2, and 3 were 51%, 49%, and 42%, respectively, in 1997, and 33%, 31%, and 22%, respectively, in 1998. The variable rate treatment had lower coefficients of variations than the other two uniform rate treatments in both years. The uniform N and P treatment resulted in smaller yield variations than the uniform N treatment in both years, though the differences between the two treatments were very small.

Table 6. Analysis of variance of grain sorghum yields (kg/ha) for three fertilizer treatments assigned among six blocks in a randomized complete block design in 1997 and 1998. Year

Source

DF

Sum of Squares

Mean Square

F–value

Pr > F

R2

1997

Model Block Treatment Error Corrected total

7 5 2 10 17

2800286 2246483 553803 919139 3719425

400041 449297 276901 91914

4.35 4.89 3.01

0.018 0.016 0.095

0.753

1998

Model Block Treatment Error Corrected total

7 5 2 10 17

2807710 970133 1837576 977372 3785082

401101 194027 918788 97737

4.10 1.99 9.40

0.022 0.167 0.005

0.742

Vol. 44(2): 201–209

207

East Field (1997)

East Field (1998)

Figure 6. Grain sorghum yield maps from yield monitor data for one of the three fields for a variable rate application study in 1997 and 1998 (1–uniform rate N, 2–uniform rate N and P, and 3–variable rate N and P). Table 7. Coefficients of variation (%) of grain sorghum yields for three fertilizer treatments among six blocks in 1997 and 1998. Block Year

Treatment

1

2

3

4

5

6

All

1997

1–uniform N 2–uniform N&P 3–variable N&P

62.7 43.7 27.4

51.7 46.6 47.0

38.7 50.3 33.2

57.8 48.4 46.0

45.1 51.6 42.0

46.9 42.3 44.2

50.9 48.5 41.9

1998

1–uniform N 2–uniform N&P 3–variable N&P

39.4 19.8 15.9

23.9 49.5 19.9

29.4 36.8 24.7

36.9 18.6 23.6

19.5 31.0 19.4

34.1 24.0 22.1

33.0 31.4 21.7

ECONOMIC ANALYSIS Table 8 shows results of a simple economic analysis of the three fertilizer treatments in 1997 and 1998. Two different grain prices were used in this analysis because grain prices varied from a low of $0.10/kg to a high of $0.15/kg in the last few years in south Texas. Relative economic returns of the variable rate N and P treatment over the uniform N and P rate treatment were from $27 to $47/ha in 1997 and from $23 to $40/ha in 1998. However, both treatments 2 and 3 had negative economic benefits over treatment 1 in 1997 because of extremely low yields and additional fertilizer costs. As mentioned previously, severe weed infestations in blocks 3 and 5 resulted in lower mean yield for treatment 2 than for treatment 1. Moreover, higher fertilizer costs were incurred in treatment 2. Although treatment 3 yielded higher than treatment 1, the increased yield did not cover the additional fertilizer costs for treatment 3. In 1998, the variable rate treatment had positive economic returns of $19 to $59/ha over treatment 1, while treatment 2 had an economic return from –$4 to $18/ha over treatment 1. The break–even grain price would be $0.11/kg. It should be noted that this economic analysis did not consider the costs associated with soil sampling, additional equipment, and data analysis. The actual economic returns of the variable rate treatment would be much lower or even

208

negative if these costs were considered. Of these costs, only the cost for soil sampling can be accurately determined. The costs for additional equipment and data analysis are difficult to estimate. Nevertheless, it is certain that these costs will go down if the same equipment and data analysis software are used for more fields over more years. Economic benefits from variable rate application can only be derived from increased yields and/or savings in reduced inputs. The variable rate fertilizer strategy employed in this study resulted in increased grain yield, but it did not reduce fertilizer costs because it aimed to achieve uniform yield goals. However, large yield variability existing in the variable rate plots indicates that less fertilizer could be used for low yielding areas, thus reducing fertilizer costs and further enhancing economic returns.

SUMMARY AND CONCLUSIONS The results from this study demonstrated that variable rate fertilizer application could increase crop yield and economic returns and reduce yield variability. However, these results are still preliminary and only reflect the performance of the particular fertilizer strategies on these three fields for two years. Additional experiments are needed to test more strategies at more fields.

TRANSACTIONS OF THE ASAE

Table 8. Economic analyses of three fertilizer treatments at two grain prices in 1997 and 1998. Fertilizer Rate

[a] [b]

Relative Returns ($/ha) over Treatment 2

Grain Yield (kg/ha)

N32[a] (L/ha)

11–37–0 [b] (L/ha)

@$0.10/kg

@$0.15/kg

1–uniform N 2–uniform N&P 3–variable N&P

2378 2313 2713

236 376 358

0 123 175

72.46 0.00 26.78

75.71 0.00 46.78

1–uniform N 2–uniform N&P 3–variable N&P

3961 4404 4742

236 314 297

0 104 147

3.80 0.00 23.27

–18.35 0.00 40.17

Year

Treatment

1997

1998

N32 price = $0.19/L. 11–37–0 price = $0.32/L.

As this was the first variable rate fertilization research project ever conducted in the Rio Grande Valley of south Texas, the biggest challenge for this two–year study was to set yield potentials and determine total nutrient requirements for variable rate application. Obviously, using a uniform yield goal was an oversimplification of actual yield potentials. The yield goal set in 1997 was not reached partially because of stand loss caused by heavy rainfall. In 1998, the yield goal was reached in some areas of the fields, but large yield variability existed in the experimental plots, including the variable rate plots. This clearly indicates that variable yield potential maps are more appropriate for these fields. Many approaches, such as grid soil sampling, yield monitoring, and remote sensing as discussed previously, have been used to determine variability and management zones for variable rate applications, yet no single approach can produce satisfactory results. For example, spatial patterns exhibited in soil maps do not necessarily match those on yield maps. Yield maps obtained from the same field over several consecutive years may show different patterns. Remote sensing has the potential to detect plant growth variability early in the growing season. Joint use of remote sensing and soil and plant sampling may allow more accurate variable rate strategies to be developed based on within–season information. Although preliminary, the results from this study show that variable rate fertilizer application has potential in the Rio Grande Valley. More experiments are needed to evaluate the long–term agronomic, economic, and environmental viability of different variable rate fertilizer strategies in south Texas. ACKNOWLEDGEMENTS The authors wish to thank Bruce Campbell and Rio Farms, Inc., of Monte Alto, Texas, for use of their fields and farm equipment and for their overall cooperation; Elsa Co–op Gin Association of Elsa, Texas, for use of its applicator and for assistance in assembling the variable rate applicator; Gerald Anderson of USDA–ARS, Sidney, Montana, for his advice on experimental design; K. Chandler of Texas Plant and Soil Lab, Inc., Edinburg, Texas, and James King, Jr., of Terra Industries, Edinburg, Texas, for providing technical assistance in determining the N and P fertilizer rates; and Fred Gomez and Wayne Swanson of USDA–ARS, Weslaco, Texas, for their field work.

Vol. 44(2): 201–209

REFERENCES Ag–Chem Equipment Co., Inc. 1997. SGIS User’s Guide, version 2.3. Minnetonka, Minnesota: Ag–Chem Equipment Co., Inc. Blackmer, A. M., and S. E. White. 1998. Using precision farming technologies to improve management of soil and fertilizer nitrogen. Australia J. Agric. Res. 49: 555– 564. Clark, R. L., and R. L. McGuckin. 1996. Variable rate application technology: an overview. In Proc. 3rd International Conference on Precision Agriculture, 855–862. Madison, Wisconsin: ASA/CSSA/SSSA. Ferguson, R. B., C. A. Gotway, G. W. Hergert, and T. A. Peterson. 1996. Soil sampling for site–specific nitrogen management. In Proc. 3rd International Conference on Precision Agriculture, 13–22, Madison, Wisconsin: ASA/CSSA/SSSA. Golden Software, Inc. 1997. SURFER for Windows, version 6. Golden, Colorado: Golden Software, Inc. Kitchen, N. R., D. F. Hughes, K. A. Sudduth, and S. J. Birrell. 1995. Comparison of variable rate to single rate nitrogen fertilizer application: corn production and residual soil NO3–N. In Proc. 2nd International Conference on Precision Agriculture, 427–441, Madison, Wisconsin: ASA/CSSA/SSSA. Searcy, S. W. 1997. Precision farming: a new approach to crop management. L–5177. College Station, Texas: Texas Agricultural Extension Service, Texas A&M University. Swinton, S. M., and J. Lowenberg–DeBoer. 1998. Evaluating the profitability of site–specific farming. J. Production Agric. 11(4): 439–446. Yang, C., and G. L. Anderson. 1996. Determining within–field management zones for grain sorghum using aerial videography. In Proc. 26th International Symposium on Remote Sensing of Environment, 606–611, Vancouver, B.C., Canada: ISRSE and SCRSS. _____. 1999. Airborne videography to identify spatial plant growth variability for grain sorghum. Precision Agric.: An Intl. J. on Advances in Precision Agric. 1(1): 67–79. Yang, C., G. L. Anderson, J. H. Everitt, and J. M. Bradford. 1998. Nitrogen and phosphorous management using a variable rate liquid fertilizer applicator. ASAE Paper No. 98–1050. St. Joseph, Mich.: ASAE. Yang, C., J. H. Everitt, and J. M. Bradford. 1999. Comparison of uniform and variable rate fertilizer applications using a variable rate liquid applicator. ASAE Paper No. 99–1145. St. Joseph, Mich.: ASAE.

209

210

TRANSACTIONS OF THE ASAE