Georgia Cotton Commission and the Flint River Water

Planning and Policy Center for supporting this work.

**ABBREVATONS:** RKN
(Southern root-knot nematode); SSM (Site-

specific management); EC_{a} (Apparent
soil electrical

conductivity), EC_{d }(Soil electrical conductivity –
Deep);

VWC (Volumetric water content)

Site-specific management (SSM) of cotton (*Gossypium hirsutum *L.*)*
fields may reduce yield losses and pest management costs. Effectively
implementing SSM requires identification of the factors contributing
to yield variability. Several factors may limit potential yield and
can interact to exacerbate yield losses; therefore, it is necessary
to determine the combined influence of yield limiting and reducing
factors on crop growth and yield. The objectives of this study were
to: i) use multiple regression analysis to evaluate the relationship
between cotton yield, soil physical and chemical properties, and
southern root-knot nematode [*Meloidogine incognita (Kofoid &
White*) Chitwood] pressure (population density); ii) integrate the
significant variables from the cotton yield regression model into a
logistic regression model to predict the probability of cotton yield
losses; and iii) utilize the cotton yield loss response model to
develop maps depicting different probability levels of yield loss
which could be transformed into management zone (MZ) maps. The
effects of soil physical properties (apparent
soil electrical conductivity - EC_{a}, slope, soil texture,
and elevation), soil chemical properties (P, K, Ca, Mg, and soil pH),
and disease [southern root-knot nematode (RKN)]
on cotton yield were evaluated in two cotton fields in
southern Georgia, USA, in 2006. Multiple linear regression and
logistic regression were used to develop a site-specific response
model of cotton yield and a probabilistic model depicting the factors
associated with the risk for cotton yield losses. The models
indicated that the percentage of sand in the soil, measured
indirectly by EC_{d}, was the
most yield limiting factor. However, the presence of aggregated high
population densities of RKN in coarse textured areas exacerbated
yield losses due to the conjunction of low uptake of water and K by
nematode infected plants and the low availability of these resources
in sandy areas. The results indicated that the need for RKN
management not only depends on nematode population density, but also
on soil texture and the interaction between soil texture and the
nematode. Maps of probability of risk for yield losses based on EC_{d}
identify low and high risk areas for yield losses, thereby providing
the producers with a basis for utilizing SSM as a means to better
allocate on-farm resources and maximize profitability.

**KEYWORDS**:
*Gossypium hirsutum *L, logistic regression, multiple linear
regression, *Meloidogine incognita*,precision agriculture,
apparent soil electrical conductivity, southern root-knot nematode,
site specific management, yield spatial variability

**INTRODUCTION**

In the last two decades, cotton (*Gossypium hirsutum L.)*
production in the Southern Coastal Plain of Georgia, USA, has grown
from 50,000 harvested hectares in 1983 to 580,000 harvested hectares
in 2006. Cotton growth is
continuously impacted by several biotic and abiotic factors. The soil
physical properties, texture and structure, determine permeability
and greatly influence rates of infiltration and plant-available water
and nutrient uptake. Consequently, soil texture and structure are
seminal factors affecting crop yield. Management of weeds and insects
has been facilitated in recent years by utilization of transgenic
cotton varieties. However, homopteran and hemipteran insects,
seedling diseases, and nematodes still challenge cotton producers and
impact yield and quality.

The continuing development of site-specific
management (SSM) has facilitated the evaluation of cotton yield
variability and permitted the spatial correlation of yield with soil
factors and terrain. Several studies have shown the interaction of
soil and landscape properties with cotton yield. Iqbal et al. (2005)
showed that elevation, flow direction, sediment transport,
sand content, and volumetric water content explained a high
percentage of cotton lint variability. Terra et
al. (2003) explained up to 60% of yield variability using apparent
soil electrical conductivity (EC_{a}), slope, soil texture,
and elevation. Corwin et al. (2003) developed a site-specific
response model of cotton yield where salinity (indirectly measured by
EC_{a}), plant-available water, leaching fraction, and pH
were the most significant properties impacting cotton yield. Other
studies have shown that both physical factors (relative elevations
and sand content) and plant nutrient status of soils are highly
correlated with cotton yields (Cox et al., 2005, Ping et al., 2005).
Apparent soil electrical conductivity (EC_{a}) has been
included in many studies as an explanatory variable for cotton yield
because of its power as surrogate data for assessing differences in
soil texture.

Cotton yield is also impacted by biotic
factors. Southern root-knot (*Meloidogyne incognita*) has
caused the highest yield losses of any pathogen in the US Cotton Belt
during the last two decades (Koenning et al., 2004). Recent studies
in Georgia have indicated that areas at risk for presence of the
Southern root-knot (RKN) can be identified by using EC_{a}
and soil spectral reflectance data, both indicators of soil textural
changes (Ortiz et al., 2006, Ortiz et al., 2007). Monfort et al.
(2007) explained 65 - 86% of cotton yield variability measured in
plots of similar geographic locations using initial population of RKN
and sand content.

The objectives of this study were to: i) evaluate the relationship
between cotton yield, soil physical and chemical properties, and
root-knot nematode populations; i) use multiple regression analysis
to evaluate the relationship between cotton yield, soil physical and
chemical properties, and southern root-knot nematode [*Meloidogine
incognita (Kofoid & White*) Chitwood] pressure (population
density); ii) integrate the significant variables from the cotton
yield regression model into a logistic regression model to predict
the probability of cotton yield losses; and iii) utilize the cotton
yield loss response model to develop maps depicting different
probability levels of yield loss which could be transformed into
management zone (MZ) maps. The basic hypothesis
was that cotton growth and development varies spatially due to the
variability in soil physical and chemical properties, as well as
nematode population density. A better understanding of factors
contributing to yield losses could lead to improved cotton management
strategies.

**MATERIALS
AND METHODS**

**Study field description and data collection**. Two fields
located in the Little River Watershed, in the Southern Coastal Plain
of south central Georgia, USA, were selected for this study in 2006.
The CC field was 20 ha and the CMP field was 25 ha. The fields were
planted with ‘Delta & Pineland (DPL) 555 Boll-Guard^{®},
Round-Up-Ready^{®}’ cotton.

Discrete data were collected on a square grid (0.20 ha). Sample locations were georeferenced using a Trimble AgGPS 114 DGPS receiver. Around the center of each grid cell (1.5 m radius), five soil samples were collected and combined for phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and soil pH determination. Soil samples were collected 30 days after planting. Volumetric water content (VWC) was collected using Time Domain Reflectometry (TDR) equipment twice during the growing season (first square and flowering).

Soil samples for RKN second stage juveniles were collected three times during the growing season: July-August (RKN S1), September (RKN S2), and November (RKN S3). At each sample location, eight individual subsamples were collected from the root zone within a 1.5 m radius and combined. Root galling was evaluated on a 0 to 10 scale after harvest by digging and rating five plants within each grid cell. In the scale of rating used, the value of 0 corresponded with no galls and 10 indicated 100% of the root system galled (Davis and May, 2005).

Continuous apparent soil electrical conductivity (EC_{a})
[shallow (0-30 cm)- EC_{s}, deep (0-90 cm)- EC_{d}]
data were collected prior to planting using the VERIS^{®}
3100 implement. An AgGPS 214 real-time kinematic (RTK) Trimble GPS
receiver mounted on the tractor pulling the VERIS^{®}
3100_{ }implement was used to collect topographic (elevation)
data. The data set comprised approximately 7000 points of EC_{a}
and elevation per field. The spatial variability of lint mass, cotton
yield in subsequent references, on each field was recorded using an
Ag Leader cotton yield monitor system (Ag Leader Technology, Ames,
IA) installed on a 9965 four-row John Deere picker. The system used
an AgGPS 132 DGPS receiver with differential correction to calculate
the position of the harvester at any time in the field.

**Data processing. **To match elevation and EC_{a} at the
RKN and soil sampling locations, continuous surface maps of 1 m^{2}
grid size for elevation and EC_{a} were created by ordinary
punctual kriging using the Geostatistical Analyst extension on
ArcVIEW 9.2. Although this procedure tends to smooth the data set,
the density of data points used suggests that the impact of
estimation at unsampled locations was minimal. Slope maps were
derived from the elevation maps. Next, buffer areas of 4 m radius
were created around each RKN sampling location. Pixel values from the
surface maps (EC_{a}, elevation, slope) within the buffer
were averaged. An inverse distance weighted (IDW) average of raw
cotton yield data within the buffer area was calculated instead of
using interpolated data which sometimes are smoothed by the
interpolation method.

**Statistical analysis. **When RKN data departed from normality,
data were log-transformed. Descriptive statistics of soil physical
and chemical properties, RKN, VCW, and cotton yield at CC field (n =
99) and CMP field (n = 98) were calculated. Pearson’s
correlation coefficients (*r*) were calculated between measured
RKN, soil physical and chemical properties and cotton yield. Soil
chemical properties included P, K, Ca, Mg, and soil pH. Soil physical
properties included elevation, slope, VCW, and EC_{a}.
Because VWC and RKN data were collected several times during the
growing season, the sampling event best correlated with cotton yield
was selected for regression analyses. Covariates in the data set were
identified using the variance inflation factor (VIF) in the PROC REG
model procedure in SAS (SAS Institute, 200). All variables with VIF >
7 were sequentially removed from the data set prior to the regression
analysis.

A cotton yield response model was developed using ordinary least
squares (OLS) regression to explain the influence of abiotic and
biotic factors on yield variability. The soil physical and chemical
properties, as well as RKN represented independent variables, and the
estimated average lint yield represented the dependent variable. A
forward variable selection procedure in SAS was used to eliminate
variables that did not significantly contribute to yield response
(*alpha = 0.10*) and to alleviate the inherent multicollinearity
between some of the predictor variables. Semivariograms of the
residuals from the OLS model were constructed to study the spatial
correlation of the errors. When errors exhibited spatial dependency,
a restricted maximum-likelihood approach was employed to evaluate the
significance of the estimated, spatial-error parameters. This
procedure simultaneously estimates the model parameters and spatial
error parameters.

Next, a stepwise logistical regression analysis was used to determine
which biotic (RKN) and abiotic (soil properties) factors contributed
to the highest probability (*P*) of having cotton yield less
than the mean cotton yield for a particular field and to establish *P*
as a function of the significant factors. The model used for the
logistic regression was *P* = e^{[(α+β*X)+kriged
residuals]} / (1 + e ^{[(α+β*X)+kriged residuals]})
(*alpha = 0.15*). The significance of each regression model was
evaluated using a likelihood ratio (–2LogL) with an
approximated chi-square distribution. In this case, the lower the
–2LogL of a model the better the model. Models with one, two,
three, and four variables (resulting from the model selection
procedure) were compared to evaluate the impact of each variable or
combination of variables on the explanation of the probability of
yield loss. The process of comparison was based on the evaluation of
the Akaike information criterion (AIC), -2LogL, and probability level
(*alpha=0.15*). The lower the –2LogL and AIC of a model,
the better it explain the data. A McFadden coefficient (R^{2}_{MF})
was calculated as a measure of fitness (R^{2}) for the
logistic regression (Menard, 2000).

The degree of spatial correlation of the residuals was also used as an additional criterion of model selection. The spatial dependence of the residuals of the logistic model was inspected using semivariograms. When the null hypothesis for errors, i.e. that the errors were not spatially correlated was rejected, the kriged residual predictions were added to the model.

For each variable used in the logistic regression model a surface map (raster map) was generated in ArcVIEW 9.2. The prediction formula of the final logistic model was then used with the ArcVIEW map calculator routine to produce the probability of yield losses map

**RESULTS AND DISCUSSION**

**Cotton yield response model. **The variables that were most
highly correlated with cotton yield at the CC field included
elevation, Ca, EC_{d}, VWC measured in September, and
Log_{10}RKNS2 (-0.67, 0.61, 0.59, 0.57, and -0.52
respectively). Although elevation was the variable with the highest
negative correlation with yield, the small changes in the field,
coefficient of variation (CV) of 2.1%, and the negative correlation
with EC_{a} which contradicts previous findings (Ortiz et
al., 2007) lead to the non-inclusion of elevation into the cotton
yield response selection model. The negative correlation of elevation
with yield could be due to a rapid movement of water from areas with
high to low elevations in the field leaving higher elevations drier.
Although Ca was not below recommended levels (< 335 kg/ha, low
level for the Coastal plain soils) 30 days after planting, the low
cation exchange capacity (CEC) in the area with the lowest EC_{d}
might indicate that Ca was not retained by the soil in this part of
the field. This result could explain the positive relation between Ca
and yield. However, because there is a significant correlation
between Ca and EC_{d} (r = 0.68), EC_{d }may be used
instead of Ca in the yield model to minimize multicollinearity. The
positive correlation between yield and EC_{d} implies that
yield decreased in areas with low EC_{d} which are mainly of
coarse texture. The negative correlation between RKN and EC_{d}
found by Ortiz et al. (2007) could also suggest an additional impact
of RKN on yield in areas with low EC_{d}. The positive
correlation between VWC and either EC_{s }or EC_{d},
0.83 and 0.84, respectively, indicated that EC_{s }and EC_{d}
could be used to explain variability in VWC with respect to soil
textural changes. The significant correlation between Log_{10}
RKNS2 and galling (r = 0.53, P < 0.05) and between galling and
cotton yield (r = -0.39, P < 0.05) evidenced the impact of RKN on
yield.

Multiple linear regression analyses indicated that EC_{d}
explained the highest percentage of variability in yield (partial r^{2}
= 0.35). The multiple linear regression for the CC field resulted in
the equation: cotton yield = 759.25 – 66.17*(Log_{10}
RKNS2) + 90.93*(EC_{d}) + 63.19*(Slope) + 2.35*(K) + ε,
R^{2} = 0.54. The positive relationship between cotton yield
and EC_{d} indicates that yield increases with increasing
EC_{d}. Because EC_{d} is highly, negatively
correlated with sand content (r = -0. 95, data not shown), this data
re-emphasized that soil texture is the primary yield limiting factor.
A secondary contributor to yield reduction was Log_{10}
RKNS2, explaining 12% of the variability in yield. This result
indicates that Log_{10} RKNS2, with the highest mean of RKN
population density during the growing season, was more useful for the
explaination of yield variability than the RKN S1 and RKN S3. The
negative relationship between RKN and cotton yield indicated that
increasing RKN densities can lead to a reduction in yield. The
preference of RKN for sandy areas with low EC_{a} reported by
Monfort et al. (2007) and Ortiz et al. (2007) explains the high
significance of these two variables on the cotton yield model

The remaining variables in the model, slope and K, explained 2 and 5%
of the variability in yield, respectively. The positive relation
between cotton yield and slope observed in this field contradicts
previous studies were this relation was negative (Kravchenko et al.,
2000). This could be due to its low spatial variability throughout
the field. Although the CV of 48% indicates a moderate variation,
high values of slope were localized. Potassium was deficient in 39%
of the total area of CC field with values less than 78 kg/ha. The
marginal availability of K supports the positive correlation (*r*
= 0.37, *P* < 0.05) of K with cotton yield in the response
model. The variables EC_{s}, Ca, and Mg were not included in
the regression model due to the high covariance with other variables
within the data set.

At the CMP field, the most highly correlated variables with cotton
yield were EC_{d}, VWC measured in September and K (0.59,
0.56, 0.48, respectively). As observed for the CC field, the good
correlation between VWC with either EC_{s} or EC_{d},
0.48 and 0.63, respectively, supported the relationship between VWC
and observed variability in soil texture. The low correlation between
Log_{10} RKNS2 and galling (r = 0.14), and between galling
and cotton yield (r = -0.11), suggested that RKN did not
significantly reduce cotton yields in this field this year because
RKN populations were reduced by rotation with the non-RKN host crop,
peanut (*Arachis hypogaea *L.) in the field the previous year.

Multiple linear regression analyses for the CMP field resulted in the
equation: cotton yield = –1702.77 + 9414*(EC_{d})
+24.83*(Elevation) + 2.22*(K) + 19.09*(VWC) + ε, R^{2}
= 0.43. The coefficients from this model were adjusted using a
restricted maximum likelihood approach due to a spatially correlated
error term. As observed for the CC site, the factor explaining the
highest percentage of variability in yield was EC_{d }(partial
r^{2} = 0.35). The positive relationship between cotton yield
and EC_{d} indicated that high yield areas can be associated
with areas of high EC_{d }and likely relatively higher clay
content. In this field as well as in the CC field, sandy soils with
low EC_{d}, have correspondingly lower soil water holding
capacities compared to heavier textured soils, thus low EC_{d},
indicates lower plant-available water in these areas. The remaining
variables in the model (VWC, elevation and K) were positively related
with yield, explaining from 2–5% of the variability in yield.
Volumetric water content was a secondary factor affecting yield at
this site. Keeping this in mind, VWC was highly correlated with EC_{d
}( r = 0.63, P < 0.0001) suggesting that the interactive
effects of water management and soil texture may significantly affect
yield.

**Probabilistic model for cotton yield losses.** While multiple
linear regression models identified the factors influencing within a
field observed cotton yield, logistic regression of these factors
allowed estimation of the probability of yield losses. The logistic
model also identified the relative weight of each factor on the
probability of yield losses, and facilitated the generation of
predictive maps depicting different levels of probability (risk) for
yield losses which could then be used for a SSM. The Akaike
information criterion (AIC), and the likelihood ratio (–2LogL)
of the logistic models for the CC and CMP fields are presented in the
tables 1 and 2 respectively.

**CC Field. **The results from the logistic regression for the CC
field indicated that EC_{d }was the single variable with the
lowest AIC value (74.3) and lowest likelihood ratio (–2LogL =
70.3). Soil electrical conductivity deep (EC_{d}) alone,
explained 48% of the variability in yield losses based on the
following logistic regression equation:

*P*_{(cotton
yield < mean yield of the field) }= *e
*^{[(5.953-6.943*EC}_{d}^{)+kriged residuals]}
/ (1 + *e* ^{[5.953-6.943*EC}_{d}^{)+ kriged
residuals]}). [ 1 ]

The
second best indicator of yield loss was VWC, having an AIC = 90.4.
The Log_{10} RKNS2 was also significant, but exhibited a much
higher AIC and likelihood ratio than EC_{d} and VWC (AIC =
117.4, –2LogL = 113). The remaining variables exhibited AIC
values > 117.4.

Two
variable models improved the explanation of yield loss only slightly,
compared to the single variable model using EC_{d}.
Significant improvements to model estimates of yield loss were
observed when EC_{d} and VWC were added to the single
variable model of Log_{10} RKNS2 (AIC = 73.1, –2LogL =
67.1 and AIC = 84.3, –2LogL = 78.3 respectively) (Table 1).
Although the likelihood ratio was reduced with additions of
individual variables to the model of EC_{d}, only the models
including Log_{10} RKNS2 or VWC were significantly different
from the EC_{d} model (AIC = 73.1, –2LogL = 67.1 and
AIC = 73.0, –2LogL = 67.0 respectively, *P* < 0.15 ).
The additions of Log_{10} RKNS2 or EC_{d }to the
model of VWC were also significant (–2LogL = 78.3 and –2LogL
= 67.0 respectively). The presence of RKN in the two variable models
demonstrates that the probability of yield losses increases when
nematodes are present in sandy areas, locations which are
also short in supply of water and nutrients. Soil electrical
conductivity deep (EC_{d}) and RKN, combined in a two
variable model, explained 51% of the variability in yield losses
based on the following logistical regression equation:

*P*_{(cotton
yield < mean yield of the field) }= *e *^{[(4.266+
0.854*LogRKN -6.747*EC}_{d}^{)+kriged residuals]}
/ (1 + *e* ^{[4.266+ 0.854*LogRKN -6.747*EC}_{d}^{)+
kriged residuals]}) ; [ 2 ]

From
the three variable models, two models explained significantly more of
the variability in yield losses compared to the single variable model
using EC_{d}. These two models included: 1) Log_{10}
RKNS2+ EC_{d} + VWC (AIC = 71.4, –2LogL = 63.4, R^{2}_{MF
}= 0.54) and 2) VWC + P+ EC_{d} were significant (AIC =
72, –2LogL = 64.0, R^{2}_{MF }= 0.53). Based on
the previous analysis, yield losses may be expected in soils with low
soil water retention (i.e. coarse textures, low EC_{d}).
Model 1 [ 3 ] suggests that yield losses associated with sandy soil
and low VWC may be exacerbated by an increment in the population
density of RKN. The significance of phosphorous in the second model,
VWC + P+ EC_{d} reinforces the importance of fertility
management and its contribution to yield loss. The logistic
regression model with three variables that exhibited the highest
percentage of prediction of cotton yield losses was Log_{10}
RKNS2+ EC_{d} + VWC:

*P*_{(cotton
yield < mean yield of the field) }= *e *^{[(8.428+
0.919* LogRKN-5.491*EC}_{d}^{- 0.626*VWC}_{
}^{)]} / (1 + *e* ^{(8.428+ 0.919*
LogRKN-5.491*EC}_{d}^{- 0.626*VWC)}) ; [ 3 ]

A
single four variable model integrating Log_{10} RKNS2, EC_{d}
, VWC , and P ((AIC = 70.4, –2LogL = 60.4, R^{2}_{MF
}= 0.56) improved estimates of yield losses only slightly (Table
1):

*P*_{(cotton
yield < mean yield of the field)}= *e
*^{[(5.33-0.512*VWC+0.03*P+0.939*LogRKN-6.074*EC}_{d}^{)}
/ (1 + *e* ^{(5.33-0.512*VWC+0.03*P+0.939*LogRKN-6.074*EC}_{d}^{)})
; [ 4 ]

No
other models significantly improved estimates of yield loss compared
to the single variable EC_{d} model.

Even
though the probability of yield losses was not sufficiently explained
by EC_{d}, the single model of yield losses based on EC_{d
}is the most informative of the models . The prediction of areas
at risk for yield losses based on EC_{d }may also be used as
an indication of areas at risk for high nematode population and lack
of nutrients which leads an increase on the probability of yield
losses. However, the inclusion of RKN population density, soil
fertility and water content factors into the logistic regression
model increases the prediction of the probability of yield losses.

The results of these logistic regression analyses provide evidence that the factors influencing yield loss comprise two categories: 1) manageable factors (i.e.water, fertility and disease control) and 2) edaphic features (i.e. soil texture). While edaphic features are not easily managed or changed, water management by changing the frequency of irrigation, fertility and disease control can be managed to alleviate some potential yield losses.

Maps of the CC field showing the spatial distribution of yield losses
based on a single variable, EC_{d }(Figure 1b); and the
combination of the best three significant variables (Figure 1c)
indicated that the zone with the highest probability of yield losses
corresponded with the zone of lowest cotton yield (Figure 1a). When
the maps of the spatial distribution of cotton yield, probability of
yield losses (Figure 1b-1c), and EC_{d }(Figure_{ }1d)
were compared, a series of similarities were found. In the map of
cotton yield, the zone with less yield than the mean (< 1126
kg/ha) agrees with the zone of the lowest EC_{d }values
(Figure 1d). This may explain why EC_{d }was the variable
with the highest contribution on the cotton yield response model.
Previous studies have shown that low EC_{d }has been
associated with coarse texture soils at the Southern Coastal plain of
Georgia (Perry et al., 2007); therefore, the low soil water retention
characteristic of this type of soil texture could be considered one
of the major factors contributing to yield loses at the CC field.

When the map of Log_{10} RKNS2 (Figure 1e) was compared to
the cotton yield map, the RKN map exhibited more spatial variability
than the cotton yield map, contrasting with the significance of Log_{10}
RKNS2 in the cotton yield and logistic regression models. The zone of
highest yield loss did not overlay exactly with the areas of high
population density of RKN; therefore, other factors appear to be
influencing yield losses caused by RKN. However, the fact that yield
losses in cotton could increase by the presence of RKN in coarse
textured soils, with low EC_{a }values, shows the importance
of a probabilistic map of yield losses based on EC_{d }as the
major contributing factor._{ }

Similarities between the maps of cotton yield, probability of yield losses and K content were also found (Figure 1f). The zone with the highest yield losses corresponded to a zone with K levels less than 78 kg/ha. This value is considered low for the Coastal Plain soils thereby contributing to plant stress and exacerbating problems with high or low micronarie. However, this zone also corresponded with an area of coarse textured soils which may contribute to K deficiency due to leaching. Potassium stress in cotton fields decreases yield and lint weight per boll, and may adversely affect micronaire, reducing fiber length and strength (Makhdum et al., 2004; Read et al., 2006). In contrast, cotton growing in areas with more sufficient K levels, produced higher yields than areas with less soil K.

The combined effects of sandy soil textures, high RKN population density and low levels of K contributed to the less than average yields observed. These yield limiting and reducing factors can be grouped as edaphic factors (coarse texture) and management factors (RKN, water, and K) which may imply a need for different strategies depending of soil texture for avoiding yield losses.

Maps of probability of yield losses based on EC_{d }can be
used as a basis for management zone (MZ) delineation. The MZ maps
depicting different levels of risk for yield losses can be used for
implementation of different strategies of water and fertilization
management and guidance for nematode sampling to target specific
areas for nematicide application.

**CMP Field. **The results from the logistic regression for the
CMP field indicated that EC_{d }was the single variable with
the lowest AIC value (97.2) and lowest likelihood ratio (–2LogL
= 93.2). As also observed for the CC field, electrical conductivity -
deep (EC_{d}) alone, explained 31 % of the variability of
yield losses based on the following logistic regression equation:

*P*_{(cotton
yield < mean yield of the field) }= *e
*^{[(4.663-3.092*EC}_{d}^{)+kriged residuals]}
/ (1 + *e* ^{[(4.663-3.092*EC}_{d}^{)+kriged
residuals]}) ; [ 5 ]

The
second best indicator of yield loss was VWC, having an AIC = 118.5
and –2LogL = 114.5. Potassium was also significant (R^{2}_{MF
}= 0.15), but exhibited a much higher AIC and likelihood ratio
than EC_{d} and VWC (AIC = 119.0, –2LogL = 115.0)
(Table 2). The Log_{10} RKNS2 exhibited the highest AIC and
likelihood ratio (AIC = 132.0, –2LogL = 128.0) indicating its
low contribution to observed yield losses. Data suggest that the RKN
population density at the CMP field did not build up fast enough
early in the growing season to cause a significant impact on yield.

The two variable models only improved the explanation of yield losses
slightly, compared to the single variable model using EC_{d.
}Slight improvements to the model estimate of yield loss were
only observed when Log_{10} RKNS2 was added to the single
variable model of EC_{d} (AIC = 96.4, –2LogL = 90.4).
However, when the map of yield was compared with the map of RKN,
there was not much agreement between the two maps. The logistic
regression model of EC_{d} and Log_{10} RKNS2
explained 33% of the variability in yield, less than the mean yield
based on the following logistic regression equation:

*P*_{(cotton
yield < mean yield of the field) }= *e
*^{(6.414-2.933*EC}_{d}^{-0.8525*LogRKN)}
/ (1 + *e* ^{(6.414-2.933*EC}_{d}^{-0.8525*LogRKN)};
[ 6 ]

The
K + EC_{d} model was the second best significant model
between the group of two variable models compared to the single
variable EC_{d }model. The models with three and four
variables did not significantly improve estimates of yield loss
compared to the single variable EC_{d }model. The logistic
regression model using all the contributing variables explained 34%
of the cotton yield losses:

*P*_{(cotton
yield < mean yield of the field) }= *e *^{(7.018-0.785*
LogRKN -0.154*VWC-0.0089*K-2.466*EC}_{d}^{)} /
(1 + *e* ^{(7.018-0.785* LogRKN
-0.154*VWC-0.0089*K-2.466*EC}_{d}^{)}) [ 7 ]

As well as in the CC field, the results from the logistic regression analyses at the CMP field indicated that edaphic factors (soil texture) considerably influenced yield losses. However, manageable factors (water, fertility, and diseases control) also contributed. The low percentage of yield losses explained through the suggested models indicated that other variables different from those used in these analyses may be driving the changes on yield loss. For example, nitrogen (N), having a high impact on plant growth and development, could be a limiting factor and should be included in future studies.

When
maps of predicted probability of yield losses (Figure 2b), cotton
yield (Figure 2a), EC_{d }(Figure 2d), and K (Figure 2f) were
compared, a high level of agreement was observed between the areas
with cotton yield less than the mean yield and the areas with low EC_{d
}and K values. The contribution of these variables to yield
losses could indicate that low yield may have occurred due to a low
soil water retention characteristic of coarse textured soils, low EC_{d
, }and deficiencies in K which also coincided with areas of low
EC_{d,} suggesting leaching of K in these areas.

In conclusion, the most yield limiting factor at the two studied
fields was soil texture. The presence of aggregated high population
densities of RKN in coarse textured areas exacerbated yield losses.
Therefore, the spatial distribution of soil texture, indirectly
assessed by soil EC_{a }through mobile soil sensors, will
give insights on coarse textured areas where differential management
of nematodes is needed. Although significant, the inclusion of
potassium (K) in the models of cotton yield response and probability
of yield losses only improved the predictions by a small percentage.
The importance of having a probabilistic map of yield losses based on
EC_{d }as the major contributing factor is beneficial for
identifying areas at risk not only for lack of water, but also for
high nematode population density. Maps of EC_{d} and
probability of yield losses based on EC_{d }can be used as a
basis for management zone (MZ) delineation. The MZ maps depicting
different levels of risk for yield losses can be used for
implementation of different strategies of water and fertilization
management and guidance for nematode sampling to target specific
areas requiring nematicide application.

The identification of the factors related to the spatial variability of cotton yield and the probability of yield losses facilitate their use as surrogate data for MZ delineation. The MZ will allow the farmer to use different management according to the potential yield of each zone thereby minimizing risk and optimizing on-farm resources and profitability.

**REFERENCES**

Corwin, D. L., S. M. Lesch, P. J. Shouse, R. Soppe, and J. E. Ayars. 2003. Identifying Soil Properties that Influence Cotton Yield Using Soil Sampling Directed by Apparent Soil Electrical Conductivity. Agron. J. 95(2): 352-364.

Cox, M. S., P. D. Gerard, and D. B. Reynolds. 2005. Selected soil property variability

and their relationships with cotton yield. Soil Sci. Soc. Am. J. 170(11): 928-937.

Davis, R. F., and O. L. May. 2005. Relationship between Yield Potential and Percentage

Yield Suppression Caused by the Southern Root-Knot Nematode in Cotton. Crop

Sci. 45(6): 2312-2317.

Iqbal, J., J. J. Read, A. Thomasson, and J. N. Jenkins. 2005. Relationships between soil-landscape and dryland cotton lint yield. . Soil Sci. Soc. Am. J. 69: 872-882.

Koenning, S. R., T. L. Kirkpatrick, J. L. Starr, J. A. Wrather, N. R. Walker, and J. D.

Mueller. 2004. Plant-Parasitic Nematodes Attacking Cotton in the United States: Old and Emerging Production Challenges Plant Disease 88(2): 100-113.

Kravchenko, A. N., D. G. Bullock, and C. W. Boast. 2000. Joint multifractal analysis of crop yield and terrain slope. Agron. J. 92: 1279-1290.

Makhdum, M. I., H. Pervez, and M. Ashraf. 2004. Effects of potassium rates and sources

on fiber quality parameters in four cultivars of cotton grown in aridisols. J plant nutr. 27(12): 2235-2257.

Menard, S. 2000. Coefficient of determination for multiple logistic regression analysis.

The Am. Stat. 54: 17-24.

Monfort, W. S., T. L. Kirkpatrick, C. S. Rothrock, and A. Mauromoustakos. 2007.

Potential for site-specific management of meloidogyne incognita in cotton using soil textural zones. J Nematol. 39(1): 1-8.

Ortiz, B. V., D. G. Sullivan, C. Perry, and G. Vellidis. 2006. Geospatial solutions for precision management of cotton root knot nematodes. In Proc. American Soc. of Agr. and Bio. Eng. Portland, OR. 9-12 July. 2006.

Ortiz, B. V., D. G. Sullivan, C. Perry, G. Vellidis, L. Seymour, and
K. Rucker. 2007. Delineation of management zones for site specific
management of parasitic nematodes using geostatistical analysis of
measured field characteristics. In Proc. *Sixth European Conf. of
Prec. Agr. (6ECPA)*. Skiathos, GR. 3-6 Jun. 2007. Stafford, J.,
and Werner, A. (Eds.).

Perry, C., G. Vellidis, W. Page, A. Milton, and D. G. Sullivan. 2007.
Use of Veris Soil EC sensor for mapping soil texture in Georgia
cotton fields*.* *.* In Proc. Beltwide Cotton Conf., New
Orleans, LA. 9-12 Jan. 2007. Natl. Cotton Counc. Am., Memphis, TN.

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Delineating potential management zones for cotton based on yields and soil properties. Soil Sci 170(5): 371-385.

Read, J. J., K. R. Reddy, and J. N. Jenkins. 2006. Yield and fiber quality of upland cotton

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**Table 1. Akaike information
criterion (AIC) and likelihood ratio (-2LogL) of the logistic models
with one, two, three, four, and five variables and increases in the
explained deviances by the addition of a new variable . CC field **

* ^{z}
*Probability level, significant (*)at P < 0.15

* ^{y}*
Logarithm of the root knot nematode population sampled in September

* ^{x}*
Soil electrical conductivity deep

* ^{w}*
Phophorus (Kg/ha)

* ^{u}*
Potassium (Kg/ha)

**Figure 1. Maps of
the CC field including the spatial distribution of yield (a),
probability of yield losses based on EC _{d} (b), probability
of yield losses based on the model: Log_{10} RKNS2+ EC_{d}
+ VWC (c), EC_{d }(d), Log_{10} RKN S2 (e), K (f).**

**Table 2. Akaike information
criterion (AIC) and likelihood ratio (-2LogL) of the logistic models
with one, two, three, and four variables and increases in the
explained deviances by the addition of a new variable . CMP field**

* ^{z}
*Probability level, significant (*)at P < 0.15

* ^{y}*
Logarithm of the root knot nematode population sampled in September

* ^{x}*
Soil electrical conductivity deep

* ^{w}*
Potassium (Kg/ha)

* ^{u}*
Volumetric water content

**Figure 2. Maps of
the CMP field including the spatial distribution of yield (a),
probability of yield losses based on EC _{d} (b), probability
of yield losses based on the model: EC_{d} + K (c), EC_{d
}(d), Log_{10} RKN S2 (e), K (f).**

See more of Advances in Nematode Management in Cotton

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See more of The World Cotton Research Conference-4 (September 10-14, 2007)

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