Spatial and Temporal Variability of Corn Grain Yield - Springer Link

Precision Agriculture, 2, 359–376, 2000 © 2001 Kluwer Academic Publishers. Manufactured in The Netherlands.

Spatial and Temporal Variability of Corn Grain Yield: Site-Specific Relationships of Biotic and Abiotic Factors S. MACHADO,1 * E. D. BYNUM, Jr.,1 T. L. ARCHER,1 R. J. LASCANO,2 [email protected] L. T. WILSON,3 J. BORDOVSKY,1 E. SEGARRA,1 4 K. BRONSON,1 D. M. NESMITH,1 AND W. XU1 4 1 Texas A&M University System, Texas Agricultural Research and Extension Center, Rt. 3, Box 219, Lubbock, TX 79403 2 Texas A&M University–USDA-ARS, 3810 4th Street Lubbock, TX 79415, 3 Texas A&M University System, Texas Argricultural Research and Extension Center, 1509 Aggie Drive, Beaumont, TX 77713-8530 4 Texas Tech University, Lubbock, TX 79409 Abstract. Inadequate information on factors affecting crop yield variability has contributed to the slow adoption of site-specific farming (SSF). This study was conducted to determine the effects of biotic and abiotic factors on the spatial and temporal variability of irrigated corn grain yields and to derive information useful for SSF. The effects of water (80% evapotranspiration (ET) and 50% ET), hybrid (drought-tolerant and -susceptible), elevation, soil index (SI)(texture), soil NO3 –N, arthropods, and diseases on corn grain yield were investigated at Halfway, TX on geo-referenced locations. Grain yields were influenced by interrelationships among biotic and abiotic factors. Grain yields were consistently high under high water treatment, at higher elevations, and on soils with high SI (high clay and silt). Soil NO3 –N increased grain yields when water was adequate. Management zones for variable rate fertilizer and water application should, therefore, be based on information on elevation, SI, and soil NO3 –N. The effects of arthropods, diseases, and crop stress (due to drought and N) on corn grain yield were unpredictable. Spider mite (Oligonychus pratensis) and common smut (Ustilago zeae) damage occurred under hot and dry conditions in 1998. Spider mite infestations were high in areas with high soil NO3 –N. Moderate air temperatures and high relative humidity in 1999 favored southwestern corn borer (Diatraea grandiosella) and common rust (Puccinia maydis) incidences. Knowledge of conditions that favor arthropods and diseases outbreak and crop stress can improve the efficiency of scouting and in-season management of SSF. Management of SSF can be improved when effects of biotic and abiotic factors on grain yield are integrated and evaluated as a system. Keywords: differential global positioning system (DGPS), geographical information system (GIS), soil index (SI), site-specific farming (SSF), variable rate technology (VRT)

Introduction Farming based on the needs of specific areas within a field is called site-specific farming (SSF). The extensive variability in soil properties and crop productivity * Corresponding author. Currently at Oregon State University, Columbia Basin Agricultural Research Center, P.O. Box 370, Pendleton, Oregon 97801.

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(Mulla and Schepers, 1997) and the need to increase profit margins (Wollenhaupt et al., 1994) support the introduction of SSF. Concerns of excessive environmental pollution when fields are treated uniformly as in traditional farming practices also justify SSF. These practices have led to over fertilization in areas with high residual nutrients and unnecessary applications of insecticides and herbicides in areas that do not need treatment (Mulla and Schepers, 1997; Wollenhaupt et al., 1994). Furthermore, insecticide resistance may occur when whole fields are sprayed uniformly with insecticides. Evaluation and implementation of SSF has been made possible by advancements in remote sensing, global positioning systems (GPS), geographical information systems (GIS), variable rate technology (VRT), and grain yield monitors. These technologies made it possible to identify and treat specific areas within a field differently from others. SSF has the potential to revolutionize crop production by increasing profit margins through improved efficiency in the management of field variability. Despite the advancement in technology, adoption of SSF is lagging primarily because it has not proven to be more profitable than traditional farming practices (Lowenberg-DeBoer and Swinton, 1997, Lowenberg-DeBoer and Boehlje, 1996). A review of economic analyses shows that SSF was profitable only in some situations (Lowenberg-DeBoer and Swinton, 1997). However, all of these studies provided partial budgets based on inadequate input and output information and therefore cannot be reliably used to evaluate the viability of SSF. As illustrated in recent precision agriculture conferences such as Robert et al. (1996, 1998), early research on SSF has been dominated by the development and evaluation of instrumentation and VRT of fertilizers. Likewise studies on economic returns to SSF have been based on fertilizer or single factor responses. Fertilizer alone or single factors, however, have not explained the observed spatial and temporal variation in grain yields (Mulla and Schepers, 1997; Everett and Pierce, 1996; Braum et al., 1998; Solohub et al., 1996). To successfully evaluate and implement SSF, more information on factors affecting variability of grain yields is required. The observed variation in crop growth from season to season is the result of the interactions between abiotic and biotic factors experienced during a growing season (Mulla and Schepers, 1997; Braum et al., 1998). We hypothesize that implementation and adoption of SSF will be successful when biotic and abiotic factors limiting grain yields and profitability can be identified and managed at different locations and for different plant growth stages in integrated systems. The objectives of this study therefore were to (1) increase our understanding on the effects of biotic and abiotic factors on the spatial and temporal variation of corn grain yields and to (2) use this information to identify management zones for SSF. To describe the effects of biotic and abiotic factors on grain yield, the effects of water, elevation, soil texture, soil NO3 –N, hybrid, herbivorous arthropods, and diseases were evaluated in irrigated corn. Statistical procedures used to analyze the data include univariate analysis, standard least squares, Pearson correlations, factor analysis, and univariate multiple regressions.

SPATIAL AND TEMPORAL VARIABILITY OF CORN GRAIN YIELD

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Materials and methods Experimental layout This experiment was carried out in 1998 and 1999 at the Texas A&M University Agricultural Research and Extension Center at Halfway, Texas (101˚ 57 W, 34˚ 11 N; 1071 MSL) on Pullman (fine, mixed, superactive, thermic, Torretic Paleustolls) clay loam and Olton (fine, mixed, superactive, thermic, Aridic Paleustolls) loam soils with moderately slow permeability on a slope ranging from 0.5% to 1.5%. The experimental site was irrigated by a low energy precision application system (LEPA) (Lyle and Bordovsky, 1981). The field was initially divided into ∼30-m grid cells that were differentially georeferenced (DGPS) (Satloc® Precision GPS Applications, Model SLX, Scottsdale, AZ). The 30-m grid size was chosen as a compromise between the cost of analyzing a large number of samples and an adequate sampling interval (Sadler et al., 1998). After demarcating the experimental area, however, a few more DGPS locations were added between the original DGPS locations to increase or equalize the number of DGPS locations in treatment areas. In 1998, the experiment was done on 2.7 ha covering 28 DGPS locations (Fig. 1a). The remainder of the area under the LEPA was under sorghum and cotton. To increase the area under corn, ∼1.6 ha of the area that was under sorghum in 1998 was added to the corn experimental area increasing the area under corn to 4.3 ha with 33 DGPS locations in 1999 (Fig. 1b). Data was collected at each DGPS location from a 6-m radius.

Treatments The effect of water on corn grain yield was evaluated by imposing two irrigation regimes. In each year half the study area was watered either based on 50% evapotranspiration (ET) demand (low water) and or 80% ET demand (high water). The ET was calculated from reference evapotranspiration (ETo and was a function of plant development and environmental conditions. Weather variables needed to calculate ETo were measured by a weather station located 500 m from our plots. ETo was calculated by the Penman–Monteith equation (Allen et al., 1998). The study area was not stratified in 1998 because we were interested in the spatial variation of grain yields across the field as influenced by the inherent variation in soil physical conditions. Because the uppermost areas, covering about 25% of the experimental area, were exclusively under 80% ET and the lowest areas, covering 20% of the total experimental area, were exclusively under 50% ET in 1998, we decided to stratify the study area so that each water treatment was represented in both upper and lower slopes in 1999. The irrigation treatments were randomly allocated to each stratum (Fig. 1b). In 1998, two corn hybrids, P3223 (drought-tolerant) and P3225 (drought-susceptible), were grown under both water treatments (Fig. 1a). In 1999, the hybrid P3225 went out of production and was replaced by P3260, another drought-susceptible hybrid (Fig. 1b). P3223, P3225, and P3260 are Pioneer hybrids (Pioneer Hi-Bred International Inc. Des Moines, IA). Agronomic practices are shown in Table 1.

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(a) 1998

Elevation Sorghum 33.0m 32.5m

P3225 Playa lake

32.0m

50% ET

Corn P3223

31.5m

80% ET Cotton

31.0m 30.5m 30.0m 29.5m 29.0m

0.000

0.120

0.240

0.360

0.480

Kilometers

(b) 1999 Sorghum Stratum 1

Elevation

33.0m

Stratum 2

P3260

32.5m

Corn Playa lake

32.0m

P3223

31.5m

80% ET 50% ET

31.0m

Cotton 30.5m 30.0m 29.5m 29.0m 0.000

0.120

0.240

0.360

0.480

Kilometers

Figure 1. Experimental layout at Halfway, TX, in (a) 1998 and (b) 1999 showing water and hybrid treatments.

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SPATIAL AND TEMPORAL VARIABILITY OF CORN GRAIN YIELD Table 1. Agronomic practices for the corn site specific farming (SSF) experiments at Halfway, TX Year

Planting date Harvest date Hybrid Planting rate (plants ha−1 Planting depth (cm) Row spacing (m) Fertilizer (kg ha−1 Herbicides

Water (mm): 80% ET 50% ET Rainfall (mm) Total water (mm): 80% ET 50% ET

1998

1999

30 April 8 September P3223 P3225 73606 4 1 134 (32-0-0) (11 June) Harness Xtra® @ 4.7 l ha−1 (1 May) Prowel® @ 2.4 l ha−1 (4 June) 323 164 162 485 326

13 May 7 October P3223 P3260 73606 4 1 none Harness Xtra® @ 4.7 l ha−1 (14 May) 279 149 247 526 396

Sampling procedures The field was characterized by measuring relative elevation, soil texture, soil NO3 –N, and soil moisture. Because elevation from the GPS unit was not accurate, relative elevations at all the DGPS locations were determined by direct leveling with standard field surveying instruments (David White Instruments, Model 8114, Realist Inc., Menomonee Falls, WI) using the center of the LEPA as the reference point. The center of the LEPA was assigned an arbitrary elevation of 30 m to avoid negative slopes in areas lower than the base. Soil texture values at 0- to 15-, 15- to 30-, 30- to 60-, and 60- to 90-cm soil depths were determined using the hydrometer method (Milford, 1976) at all DGPS locations. A soil index (SI) was developed to represent a single measure of soil texture and was calculated as the sum of the products of percent soil texture classes and their respective water holding capacities. For example, the SI of a location with 36% clay, 23% silt, and 41% sand would be 36∗ 03 + 23∗ 024 + 41∗ 004 = 18 where 0.3, 0.24, and 0.04 are the assumed water holding capacities of clay, silt, and sand, respectively. A high SI indicates a high percentage of clay and silt fractions while a low index represents sandier soils. It follows that soils with high soil index have high water holding capacity (Brady, 1974). Before each planting, soil was sampled at the same depths at all DGPS locations and analyzed for soil NO3 –N (AutoAnalyzer II, Technicon Industrial Systems, Tarrytown, NY). Soil moisture was monitored in the 0- to 30-, 30- to 60-, 60- to 90-, 90to 120-, and 120- to 180-cm depths at all the DGPS locations bi-monthly using a neutron attenuation probe (CPN Model 503-DR, Martinez, Ca). The neutron probe was calibrated to these soils. Total plant water use was calculated as the sum of the differences between moisture readings at successive sampling times including rain and irrigation amounts. Two weeks after emergence of corn, plant density was determined by counting the number of plants along 5, 3.9-m rows centered at each DGPS location. In 1998, there

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was an outbreak of common smut and spider mites. Common smut is a fungus that infects and destroys developing kernels and reduces grain yields (White, 1999). Spider mites damage leaf epidermal cells and exacerbate drought stress by increasing nighttime transpiration and reducing stomatal opening during the day (De Angelis et al., 1982). The damage from common smut was rated by counting the number of infected plants out of 10 consecutive plants in a row centered at each DGPS location. Spider mite damage was determined by the method of Chandler et al. (1979). Plant lodging that occurred in 1999 was assessed by counting the number of plants that had fallen in 5 rows (of ten consecutive plants each) centered at each DGPS location. Lodging was considered root lodging if the roots failed to anchor the plants. If the stem broke above the crown, then plant stems of the lodged plants were split to determine the cause of lodging. If tunneling and girdling were evident then southwestern corn borer was assumed to have caused the lodging (White, 1999). Common rust was also detected in 1999 and the damage was rated on a 1–5 scale. The rating was assigned 1 when no infection was present and 5 when more than 80% of the plant had rust lesions. Data on leaf senescence and leaf firing were collected towards the end of the grain filling period. Leaf senescence was determined by counting the number of dead leaves from the bottom of the plant and leaf firing as the percentage of dead leaf tissue from the top leaves. A combine fitted with a grain yield monitor (John Deere Greenstar®, Dallas, TX) was used to harvest the crop. Data analysis Geo-referenced grain yield data were converted to grid maps using a combination of MapInfo Professional® (Version 5.0, MapInfo Corporation, Troy, NY) and SoilRx® (Version 1.3.2.1, Red Hen Systems, Fort Collins, Co) mapping software. Grain yield data corresponding to the DGPS sampling locations were then extracted from the grid map using MapInfo Professional® query. The associations between grain yields at these locations and soil, plant, arthropods, and disease factors were then determined using the following methods. Data were first subjected to univariate analysis to obtain descriptive parameters. The data were further analyzed using standard least squares procedures, Pearson correlations, factor analysis, and univariate multiple regressions (SAS Inc., 1999). The standard least squares procedures were done to compare the main and interactive effects of water and hybrid treatments. Correlations were used to find associations between measured factors and grain yield. Factor analysis is a procedure used to reveal simple underlying latent variables or common factors that are presumed to exist within a set of multivariate observations (Davis, 1986; Khattree and Naik, 2000). In other words we assume that the variables we observe are functions of common factors that we cannot observe. By identifying common factors that are presumably uncorrelated, factor analysis solves multicollinearity problems normally associated with multivariate data and makes the data more suitable for regression analysis. Factor analysis was done with the factor procedure using the principal factor analysis method. Factor loadings were rotated by the Varimax rotation method. The factors reported in this study have eigenvalues greater than 1. To evaluate the relationships between the identified common factors and grain yield, mul-


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tiple regression models were fit for each water treatment. The regression model used is expressed as Y = b0 + b1 F1 + b2 F2 + · · · + bn Fn + , where b0 to bn are coefficients and F1 to Fn are the common factors, and is residual error. The criteria for factor addition or deletion was P < 010. Results and discussion Site description The 1998 growing season was drier and received 34% less rain than the 1999 season (Table 1, Fig. 2). Elevation of the cultivated area under the LEPA dropped by 4.47 m from the top to the bottom. The lowest slopes normally referred to as foot slopes, occupy a playa lake and were not cultivated. In 1998, the area under the 80% ET treatment was located at the top and elevation in this area dropped by only 0.29 m (Fig. 1a). The corresponding drop in elevation in the area under 50% was 1.42 m (Fig. 1a). In 1999, the experimental area was widened and the water treatments were stratified so that each water treatment had a stratum both at the top and bottom of the field (Fig. 1b). The change in elevation from one stratum to the other was 0.81 m and 2.12 m for 50% and 80% ET treatments, respectively. The SI was highly and positively correlated (P < 005) with elevation in both years (r = 092 and 0.89 in 1998 and 1999, respectively) indicating that the upper slopes were higher in clay and silt fractions than the lower slopes. Soil NO3 –N in the 0–90 cm depth profile was negatively correlated (P < 005) with both SI and elevation in 1998 (r = −051 and r = −053, respectively). In contrast SI was positively correlated (P < 005) with soil NO3 –N in the same depth profile in 1999 (r = 036) indicating that soils with high SI were in this year high in soil NO3 –N. The spatial and temporal variability of soil NO3 –N was probably attributed to the previous crop N uptake, N applications, and other N dynamics. Water and hybrid main effects on grain yield Average corn grain yields varied substantially in all the years (Table 2). The variation in grain yields was more in 1998 than in 1999 particularly under the 50% ET treatment. Grain yields also varied spatially and water appeared to be the major factor influencing grain yields more so in 1998 than in 1999 (Fig. 3). On average, higher grain yields were produced under the high water treatment in both years but grain yield differences were significant (P < 005) only in 1998 (Table 3). During 1998 the high water treatment produced 70% more grain than the low water treatment. The evaporative demand in the 1998-growing season, particularly in June and July, was high (Fig. 2c, d) and substantial grain yield differences due to water treatment were expected. There were no significant differences in grain yields between the two hybrids within each water treatment even though the two hybrids differed in drought tolerance (Table 3). This indicated that drought tolerant hybrids do not always guarantee high grain yields under drought conditions.

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Temperature (ºC)

50

(a) Temperature (ºC)

40 30 20 98 99

10 0 120

Relative Humidity (%)

(b) Relative Humidity 100 80

98 99

60 40 20 0 60

14 (c) PET andRainfall for 1998

50 40

12 10

98 PET

8

30

6 20

4

10

2

0

0

PET (mm)

Rain (mm)

98 rain

12

60 (d) PET and Rainfall for 1999

Rain (mm)

99 r ain

40 30

8

99 P ET ent Station,

o

6

20

4

10

2

PET (mm)

10

50

0

0 145

165

185

205

225

245

265

285

Julian day Figure 2. (a) Minimum and maximum temperatures and (b) relative humidity in 1998 and 1999, and potential evapotranspiration (PET) in (c) 1998 and (d) in 1999 at Halfway, TX.

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Table 2. Summary statistics for corn grain yield and measured variables under each water treatment in 1998 and 1999 1998†

1999†

Variable

50% ET

80% ET

50% ET

80% ET

Grain yield (kg ha−1 , 103 ) Plant population 104 Elevation (m above ref.) Soil index (0–90 cm) Soil NO3 -N (0–90 cm) Water use (cm) Common smut damage (%) Spider mite damage (1–9) Leaf senescene Leaf firing Leaf common rust Southwestern corn borer Plant lodging

1.25 ± 0.85 (68)

4.2 ± 0.88 (21)

3.88 ± 0.77 (20)

4.01 ± 0.12 (29)

4.92 ± 0.32 (7)

5.14 ± 0.34 (7)

4.02 ± 0.39 (10)

4.17 ± 0.37 (9)

32.91 ± 0.41 (1)

33.46 ± 0.10 (0.3) 33.23 ± 0.24 (1)

32.80 ± 0.24 (1)

19.18 ± 0.96 (5)

20.43 ± 0.27 (1)

19.43 ± 1.59 (8)

20.28 ± 0.72 (4)

61.66 ± 20.48 (33) 46.32 ± 12.59 (27) 154.63 ± 55.46 (36) 181.94 ± 125.39 (67) 19.13 ± 1.52 (8) 25.98 ± 3.20 (12) 43.41 ± 4.10 (9) 46.76 ± 2.86 (6) 8.79 ± 3.54 (40) 3.46 ± 1.79 (52) – – 1.59 ± 0.93 (59)

0.63 ± 0.58 (92)

–

–

2.64 ± 0.84 (32) 3.57 ± 3.63 (102)

2.07 ± 0.27 (13) 2.14 ± 3.23 (151)

– –

– –

– –

– –

1.59 ± 0.58 (36) 2.57 ± 0.71 (28)

2.36 ± 1.23 (52) 2.87 ± 0.43 (15)

–

–

2.26 ± 0.59 (26)

1.54 ± 0.55 (36)

† variable ± SD

(CV%). (SD for Standard Deviation). –variable not measured.

In 1999 no significant differences in grain yields were observed between the low and high water treatments and between the hybrids within each water treatment (Table 3). The 1999 growing season received 59% more rain during May and June than during the same period in 1998 and the frequency of rainfall made it difficult to impose water treatments early (Fig. 2). There were no significant interactions between water and hybrid on grain yield in both years (Table 3). However, grain yield varied spatially within each water treatment indicating that other factors other than water influenced grain yields. These factors, which include elevation, SI, soil NO3 –N, water use, hybrid, plant density, common smut, spider mites, leaf common rust, southwestern corn borers, and plant lodging, are discussed below. Factors influencing grain yield within water treatments Factor analysis. The rotated components for the measured variables are shown in Table 4. Large loadings indicate substantial correlations of the variables with the common factor. The signs of the loadings describe how the variables relate to the common factor. For example a positive sign suggest that the variable varies positively with the

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(a) Corn grain yield in 1998. 80%ET

Kg ha-1 50%ET

6700 3600 2000 400 100

P3225 (1) (droughtsusceptible)

P3223 (2) (droughttolerant)

(b) Corn grain yield in 1999. 50%ET P3260 (2) (drought-

80%ET

susceptible)

50%ET

80%ET

Kg ha-1 11700 5300 4100 2900 200

P3223 (1) (droughtRep 1

Rep 2

tolerant)

Figure 3. Spatial variability of corn grain yields in (a) 1998 and (b) 1999. Numbers in parenthesis to the right of the hybrid name represent the dummy variables used in statistical analyses.

common factor and a negative sign indicates the opposite. No clear standards exist on deciding what a “large” factor loading is. The decision is purely subjective and in this paper “large” is relative to other loadings. Identification of common factors is easy in some cases where the factor loadings are obviously large but difficult in other cases. Nonetheless, factor analysis is an important tool in multivariate analysis where multicollinearity problems are common. Factors influencing corn grain yield under the 50% ET treatment in 1998. Four common factors were identified under the 50% ET treatment (Table 4a). The regression model that included all the four factors explained 71% of the spatial variation in

369

SPATIAL AND TEMPORAL VARIABILITY OF CORN GRAIN YIELD Table 3. Effect of water and corn hybrids on grain yield (kg ha−1 ) Treatment

1998

Treatment

1999

Water: 50% ET (1) 80% ET (2) Hybrid: P3225 (1) P3223 (2) SE

124566a 419929b 271986a 272509a 24044

Water: 50% ET (1) 80% ET (2) Hybrid: P3223 (2) P3260 (1) SE

387928a 401545a 401752a 385155a 22897

Water × Hybrid 1×1 1×2 2×1 2×2 SE

119879a 129254a 424093a 415764a 34004

Water × Hybrid 1×1 1×2 2×1 2×2 SE

394428a 394664a 421291a 390570a 32291

Stratum: 1 2 SE

439103a 355873b 21126

Stratum: 1 2 SE

– – –

corn grain yields (Table 5). Hybrid, plant population, water use, and common smut, respectively, assign large loadings of 0.83, 0.87, 0.77, and −0.97 (boldface) to the first factor (Table 4a). This factor can be interpreted as a measure of hybrid effects. Dummy variables 1 and 2 were used, respectively, to represent hybrid P3225 and P3223 in the analysis of 1998 data. Therefore a positive correlation or factor loading on the hybrid variable refers to P3223 and a negative correlation or factor loading on the hybrid variable refers to P3225. This factor can be interpreted to mean that P3223, a drought tolerant hybrid, positively influenced grain yields through high plant density, high water use, and tolerance to common smut damage. P3223 had 9% more plants ha−1 than P3225 that could have increased water use. Common smut damage was 47% less in P3223 than in P3225. The initial common smut infection was observed in P3225 in the upper slopes in areas under high water treatment but progressed faster in the middle slopes under the low water treatment in both hybrids. Drought stress conditions have been reported to be ideal for the prevalence of common smut (Smith and White, 1988; White, 1999). P3223 produced 7% more grain than P3225 under the low water treatment. Table 4a. Rotated factor pattern of variables under the 50% ET treatment in 1998 Variable

Factor 1

Factor 2

Factor 3

Factor 4

Hybrid Plant population Elevation Soil index (0–90 cm) Soil NO3 –N (0–90 cm) Water use Common smut damage Spider mite damage Leaf senescence Leaf firing

083 087 −007 −010 −013 077 −097 010 −016 −014

−023 007 098 093 −033 005 −006 −009 −020 −089

001 009 −012 −020 087 006 014 090 010 013

−031 −039 001 −003 −018 046 −005 030 089 027

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MACHADO ET AL. Table 4b. Rotated factor pattern of variables under the 80% ET treatment in 1998 Variable

Factor 1

Factor 2

Factor 3

Hybrid Plant population Elevation Soil index (0–90 cm) Soil NO3 –N (0–90cm) Water use Common smut damage Spider mite damage Leaf senescence Leaf firing

089 056 −084 −021 005 −008 −082 066 007 013

025 054 010 −030 −027 067 −010 −032 093 078

015 018 −042 −077 085 −018 032 037 −011 041

The second factor gets high and positive loadings from elevation (0.98) and SI (0.93) and a negative loading from leaf firing (−0.89) (Table 4a). The common factor here appears to be elevation. Soils at higher elevations were high in SI and soils at lower elevations were low in SI. Plants growing in these areas showed less leaf firing than plants growing at lower elevations. The high water supply from soils with high SI at high elevations probably minimized heat stress that normally causes leaf firing. These conditions resulted in high grain yields. Factor 3 that receives high loadings from soil NO3 –N (0.87) and spider mite damage (0.90) (Table 4a) had a negative effect on grain yield (Table 5). The third factor can, therefore, be taken as a measure of conditions favorable for spider mites development. This suggests that plants growing in areas high in soil NO3 –N were susceptible to spider mite damage that may have contributed to low grain yields in these areas. Archer, et al., (1990) also found high spider mites incidences in sorghum plants growing under high N treatments. Factor 4 positively influenced grain yields (Table 5). It receives large loadings from leaf senescence (0.89), water use (0.46), and plant density (−0.39) (Table 4a) indicating that high grain yields were obtained in areas where water use and leaf senescence were high. Increased water use may have resulted in bigger plants with more leaves than plants growing in other areas. It follows that, by proportion, more leaves on these plants would senesce compared to smaller plants with fewer leaves during periods of drought Table 4c. Rotated factor pattern of variables under the 50% ET treatment in 1999 Variable

Factor 1

Factor 2

Factor 3

Hybrid Plant population Elevation Soil index (0–90 cm) Soil NO3 –N (0–90 cm) Water use Leaf common rust Southwestern corn borer Plant lodging

−095 090 −012 −075 −016 −004 088 −006 051

009 −014 089 051 072 −044 −005 002 007

−006 001 −032 −022 022 060 −004 086 068

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SPATIAL AND TEMPORAL VARIABILITY OF CORN GRAIN YIELD Table 4d. Rotated factor pattern of variables under the 80% ET treatment in 1999 Variable

Factor 1

Factor 2

Factor 3

Hybrid Plant population Elevation Soil index (0–90 cm) Soil NO3 –N (0–90 cm) Water use Leaf common rust Southwestern corn borer Plant lodging

−003 −012 096 094 059 −013 024 −082 −026

−084 070 004 −013 003 001 088 008 066

051 012 −006 −004 055 089 −009 005 014

stress. It is also possible that more leaves on these plants senesced as a result of greater demand for assimilates during grain filling brought about by larger sinks when compared to smaller plants. Based on the signs of the loadings, leaf senescence was not associated with plant density under this water treatment. Factors influencing corn grain yield under the 80% ET treatment in 1998. Under the 80% ET treatment, three common factors were identified (Table 4b). Factors 2 and 3 explained 45% of the variability in grain yield (Table 5). The effect of factor 1 was on grain yield was not significant and therefore this factor was not included in the regression model. Factor 2 and 3 had a negative effect of grain yield. Factor 2 describes the association among plant population (0.54), water use (0.67), leaf senescence (0.93), and leaf firing (0.78) (Table 4b). Leaf senescence under 80% ET appear to be associated with plant density. The loadings in this situation suggest that soil water was depleted more rapidly in areas that had high plant populations through high water use. Under such conditions, drought stress could develop between irrigations resulting in leaf senescence, leaf firing and reduced grain yields. Although the 80% ET treatment received relatively more water that the 50% ET treatment, plants under this treatment periodically experienced both drought and heat stress because of the dry and hot conditions experienced in 1998. Factor 3 receives high and negative loadings from SI (−0.77) and elevation (−0.42) and positive loadings from soil NO3 –N (0.85) and leaf firing (0.41) (Table 4b). This factor suggests that soils low in SI, that were associated with low elevation, contained high soil NO3 –N. However, it was not apparent why these areas would cause leaf firing given the relatively high water amounts received under this treatment. It is probable Table 5. Multiple regression models of factors influencing corn grain yields in the whole field, under low and high water treatments Regression model

R2

Pr > F

50% ET 80% ET

Y = 124566 + 41808F1 + 37421F2 − 27991F3 + 34813F4 + Y = 419928 − 37084F2 − 45716F3 +

071 045

001 004

50% ET 80% ET

Y = 387928 + 48838F2 − 23519F3 + Y = 401545 + 57965F1 + 496082 +

050 043

0004 004

Year

Treatment

1998 1999

Y = grain yield, F1 F2 Fn = factors, and = residual error.

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that the high soil NO3 –N encouraged vigorous plant growth that rapidly depleted soil moisture between irrigations resulting in heat stress and leaf firing. As indicated above, the crop under the high water treatment was also susceptible to drought and heat stress because of the dry and high temperatures conditions experienced in 1998. Factors influencing corn grain yield under the 50% ET treatment in 1999. Under the 50% ET treatment, 50% of the variation in grain yields could be explained by factor 2 and 3 (Table 5). Factor 1 did not significantly influence grain yields. Factor 2, that positively influenced grain yields, describe the relationship among elevation (0.89), SI (0.52), and soil NO3 –N (0.72) (Table 4c). As in 1998, soils with high SI were located at high elevations. In contrast to 1998-growing season, however, soil NO3 –N was high in areas at high elevations that were also high in SI. The combination of high SI and high soil NO3 –N positively influenced grain yields. As indicated earlier, the spatial and temporal variations in soil NO3 –N were due to variations in N uptake, application, and movements. Factor 3, that describes the association of water use (0.60), southwestern corn borer (0.86), and plant lodging (0.68), negatively influenced grain yields (Table 5). This factor suggests that plant lodging was caused by southwestern corn borers. The effect of southwestern corn borers on plant lodging has been reported (White, 1999). The borers weaken plant stems by tunneling and girdling (White, 1999). The association of the borers and lodging with water use is, however, not clear. It is possible that plants that used up more water grew bigger and produced bigger ears that made them susceptible to lodging when infested by the borers. Factors influencing corn grain yield under the 80% ET treatment in 1999. Only 43% of the variation in grain yields could be explained by factor 1 and 2 under the 80% ET water treatment (Table 5). Factor 3 did not significantly influence grain yields. Both factors positively influenced grain yields. Factor 1 receives high and positive loadings from elevation (0.96), SI (0.94) and soil NO3 –N (0.59) and a negative loading from southwestern corn borer damage (−0.82) (Table 4d). This suggests that high grain yields were obtained at high elevations were SI and soil NO3 –N were high. These results were similar to the results obtained under low water treatment. The results also indicate that southwestern corn borer damage was low at high elevations and high at low elevations. Factor 2 receives high and positive factor loadings from plant population (0.70), leaf common rust (0.88), and plant lodging (0.66), and a high but negative loading from the hybrid variable (−0.84) (Table 4d). During this year hybrid P3223 and P3260 were represented, respectively, by dummy variables 1 and 2 in all analyses. A positive and negative coefficient (correlations or factor loadings) on the hybrid variable refers to P3260 and P3223 respectively. Factor 2 describes the hybrid effects and indicates that P3223 had (9%) more plants ha−1 than P3260. The high plant density probably increased the susceptibility of P3223 to lodging. P3223 lodged 30% more than P3260. Furthermore, P3223 was also susceptible to leaf common rust. The outbreak of leaf common rust was attributed to air temperature and relative humidity conditions experienced in 1999 (Fig. 1). Moderate air temperatures (17 to 32 C) and relative humidity >95% favor rust development and spread (Smith and White, 1988; White, 1999). The common rust outbreak, however, occurred when the crop was approaching physiological maturity and


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had little or no effect on grain yields. Despite more lodging and leaf rust infection in P3223 than in P3260, grain yields were not significantly different between these hybrids. Implications of the results to SSF Results obtained from this study confirm that the spatial and temporal variability in grain yields is influenced by multiple factors (Mulla and Schepers, 1997; Everett and Pierce, 1996; Braum et al., 1998; Soluhub et al., 1996). Some factors, such as elevation and SI, consistently influenced grain yields under both low and high water treatments. The influence of other variables, like the outbreak and spread of common smut, spider mites, southwestern corn borers, and leaf common rust, changed from year to year and were modified by drought and hybrid. Management of SSF can be improved when effects of these variables (biotic and abiotic) on grain yield are integrated and evaluated as a system. Information obtained from this study is valuable in the formulation of management decisions for SSF. According to Moran et al. (1997), information on seasonally stable conditions and variable conditions, and information required to diagnose the cause of crop yield variability in order to develop a management strategy is required to successfully implement SSF. Based on our results, elevation, SI, and soil fertility, in this case soil NO3 –N, provide information of seasonally stable or predictable conditions. Although soil NO3 –N levels change within and across seasons, we considered it predictable because, unlike diseases, it can be mapped and managed for high grain yields. Management zones for variable rate fertilizer and water application can be demarcated using information on seasonally predictable conditions. For example, a farmer with land similar to ours may consider different fertility and water treatments for soils with different SI. If a farmer decides to apply inputs for optimum productivity, we recommend intensification of fertilizer and water inputs in areas with high SI. In soils with low SI, the farmer should split apply N to reduce leaching losses and apply light and frequent irrigations. The identification of management zones based on inherent soil physical properties, like SI, although time consuming, is relatively straightforward and usually requires information from soil analysis, and survey. Electrical conductivity measurements of the soil, that provide information on soil texture and soil depth (Williams and Hoey, 1987), can be obtained faster than soil analysis results. Effects of arthropods, diseases, and crop stress due to drought or N on grain yields were seasonally variable. These effects along with seasonally predictable variables, caused the spatial and temporal variation in corn grain yields. Managing seasonally variable responses creates a major challenge for farmers because of the large number of factors involved and the uncertainty of their occurrence and crop growth responses. Nonetheless, knowing conditions that favor anthropod or disease outbreak can greatly improve the efficiency of in-season SSF management. For instance, our data indicate that common smut damage was more pronounced in areas under low water treatment. Therefore farmers can save time and money by inspecting for common smut only in areas prone to drought stress. Although common smut infestation occurred during grain filling when it was too late for a remedy, locating affected areas and assessing crop damage may help the farmer to appraise the profitability of harvesting those areas. Our

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results also indicate that spider mites prefer plants growing in areas high in soil NO3 –N under drought stress conditions. Therefore, scouting for spider mites should concentrate in those areas. The southwestern corn borer damage occurred in plants growing in low-lying areas and scouting for the borers could start in these areas. More information on plant or soil conditions that favor anthropod and disease outbreaks is needed to improve the management of SSF. Factors evaluated in our study explained only 43–71% of the variability in corn grain yields suggesting that there are many more variables that influence crop yields. Given the complexity of interrelationships of biotic and abiotic factors and their effects on crop growth and yield, monitoring plant growth offers an alternative to improving the management of SSF. Although beyond the scope of this paper, growth analysis captures the effects of biotic and abiotic factors and can be the basis for in-season management for some crops. Plant growth analysis by remote sensing techniques (Moran et al., 1997) is one of the best ways to identify trouble spots in a field. The effectiveness of management decisions based on these methods will depend on how well the causes of poor crop growth are diagnosed (Moran et al., 1997). Information on crop biomass (Belford et al., 1993), leaf area index (Bouman et al., 1992), nutrient deficiency (Thomas and Oerther, 1972; Peñuelas et al., 1994), water stress (Peñuelas et al., 1993, 1994; Ripple, 1986), insect (Riedell and Blackmer, 1999) and diseases (Nilsson, 1995; Pederson and Nutter, 1982) can be obtained from crop canopy reflectance. Crop growth models that integrate remote sensing data and biotic and abiotic interactions to calculate and predict grain yields and profitability need to be developed for use in SSF management. This study underscores the importance of assessing the impacts of biotic and abiotic factors on grain yield variability. To enhance our understanding on the causes of grain yield variability, these factors must be evaluated as a system. With this understanding the chances of successfully implementing SSF will be increased.

Conclusions Our experiments have confirmed that grain yields are not influenced by single abiotic or biotic factors but rather by variable effects among elevation, SI, soil NO3 –N, soil water, hybrid, plant density, lodging, spider mites common smut, and southwestern corn borer. Likewise management decisions for SSF should consider the effects of these factors on grain yields. Information on soil factors, like elevation, soil texture, and soil fertility (NO3 –N), that are seasonally predictable, can be used to demarcate management zones for fertilizer and water application. Information on seasonally variable factors like soil water, hybrid selection, and pests can be used to formulate in-season management decisions. Information needed to formulate in-season management decisions is complex and difficult to collect and may in the future be simplified by growth analyses facilitated by remote sensing techniques. Our results help build a foundation upon which such a program can be built. To successfully implement SSF, however, the effects of biotic and abiotic factors on crop growth and yield must be integrated and evaluated as a system.


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Acknowledgements This research was supported by the Texas State Legislature Initiative on Precision Agriculture for the Texas High Plains.

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