Exploring the spatial and temporal dimension of yellow stem borer in coastal rice ecosystem for enhanced management using geostatistical analysis

doi:10.21203/rs.3.rs-5287037/v1

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Exploring the spatial and temporal dimension of yellow stem borer in coastal rice ecosystem for enhanced management using geostatistical analysis

https://doi.org/10.21203/rs.3.rs-5287037/v1

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Rice (Oryza sativa L.), a staple food crop in tropical and subtropical regions, is highly susceptible to damage from the yellow stem borer (YSB), Scirpophaga incertulas (Walker), causing significant economic losses. This study investigates the spatial and temporal dynamics of YSB populations and associated damage in a micro-scale rice landscape in coastal Andhra Pradesh, Southeast India. A total of 5,408 YSB males were captured using pheromone traps, and significant variations in YSB populations and damage were observed across different years and months (P < 0.05). Peaks in YSB activity occurred during the tillering stage in August and from flowering to the grain-filling stage in November. Surface air temperature, humidity and rainfall showed significant positive correlations with YSB populations and damage, while remotely sensed data, including LST and NDVI, had negative associations. Geostatistical analyses revealed a strong spatial aggregation of YSB populations and damage, particularly in August and November, with spherical variogram models providing the best fit. The average spatial dependency of YSB population and damage in rice was estimated to 584.1 m and 673.33 m, respectively. Additionally, spatial autocorrelation (Moran’s I) confirmed significant clustering of YSB populations, while Getis-Ord Gi* analysis identified hotspots in southeastern locations of study area, indicating areas of higher infestation. These findings suggest that incorporating spatial data into pest management strategies can enhance the precision of insecticide applications, reduce environmental impacts and improve the overall efficiency of YSB control. This study provides valuable insights for developing more effective integrated pest management (IPM) strategies for rice cultivation.

Hotspots

Interpolation

IPM

Maps

Variogram

Rice (Oryza sativa L.) is a multi-purpose herb cultivated as an annual crop in tropical and subtropical regions for its grains. Asia is the primary producer of rice, accounting for 90% of global production, followed by the America (4.4%), Africa (5.1%), Europe (0.4%), and Oceania (0.1%). In Asia, India ranks second in rice production after China (FAOSTAT, 2022). Within India, Andhra Pradesh is the eighth-largest producer of rice, contributing approximately 5.98% to the total production (Directorate of Economics and Statistics, 2023). Rice grown in warm and humid environments foster the survival and proliferation of insect pests. More than 100 species are known to be pests of rice (Pathak, 1968) with the rice yellow stem borer (YSB), Scirpophaga incertulas (Walker) (Lepidoptera: Crambidae) being particularly severe. YSB causes significant damage, resulting in an estimated $10 million annual loss. It causes deadhearts during the vegetative stage and whiteheads during the reproductive stage, with a 2.3% yield penalty reported for every 1% of YSB damage (Muralidharan and Pasalu, 2005).

Effective pest management begins with pest surveillance, a critical component of Integrated Pest Management (IPM). Phenological and machine learning models have been developed to predict YSB population dynamics using various abiotic factors (Kumar et al., 2016; Prasannakumar et al., 2015; Reji et al., 2014; Vennila et al., 2019; Bapatla et al., 2024). However, these models often suffer from reduced accuracy and lower adoption rates, partly due to their failure to account for spatial and geographical variability (Baumgartner and Severini, 1987). Insects disperse heterogeneously within agricultural systems, and their population dynamics interact with biotic and abiotic factors, leading to spatial variation (Nestel et al., 2004; Sciarretta and Trematerra, 2014). Understanding the spatial dimension and dispersion patterns of both the agricultural system and insect pests, respectively is crucial for optimizing insecticide application in both time and space (Park et al., 2007). Brenner et al. (1998) noted that successful pest management depends not only on timing but also on the location where pest populations are most likely to be found.

Incorporating spatial dimensions into insect pest management practices could enhance control systems and reduce environmental hazards. Geostatistics, which analyze spatial dependencies among samples (autocorrelation) and use interpolation techniques to estimate variables at unsampled locations, offer a practical approach for understanding spatial data (Sciarretta and Trematerra, 2014). These methods assume that ecological data are spatially and temporally dependent, making spatial dependence a realistic and valuable concept (Rossi et al., 1992).

Previous studies have utilized geostatistics to characterize the spatial distribution of various pests, including the pigeonpea pod borer, diamondback moth, corn leafhopper, cotton stinkbug, grapevine moth, and leaf-eating beetle (Ifoulis and Savopoulou-Soultani, 2006; Sciarretta and Trematerra, 2014; Silva et al., 2021; Seethalam et al., 2021; Li et al., 2021; Foresti et al., 2022; He et al., 2023). These studies highlight the importance of understanding the spatial dimension of insect pests for effective integrated pest management in precision farming and for developing sampling strategies. However, the spatial dynamics and damage distribution of YSB in coastal rice ecosystems remain unexplored. YSB adults typically fly between 8 and 16 km (Pathak and Khan, 1994), affecting their distribution across different habitats (Saputra and Humaida, 2023; Rani et al., 2023; Horgan, 2023), the mechanisms behind this distribution are poorly understood yet. Understanding the spatial distribution and temporal dynamics of YSB at the regional level is essential for developing effective management strategies. Therefore, this study employs geostatistical methods to investigate the spatial distribution and temporal dynamics of YSB in a micro-scale rice landscape in the coastal region of Andhra Pradesh, Southeast India.

2.1 Study location

The experimental area situated in an East Coastal Plain and Hills Agro-Climatic Zone of India (Andhra Pradesh state, Srikakulam district) nearly 10km from the Bay of Bengal Sea (18°21'07.28''N − 18°25'59.42''N and 83°54'42.05''E − 84°00'07.14''E, at an elevation of 14.31m), characterized with hot summer (April to May), mild hot and humid north-west monsoon (June to October), mild cold south-east monsoon (November to March). The study area covered approximately 3,226 hectares, with rice crops distributed over 2,302 hectares, primarily cultivated during the kharif (July - December) and rabi (January - April) seasons. The landscape consists of fruit orchards (mango), plantation crops (coconut and cashew), horticulture crops (chilli, eggplant, tomato and ladyfinger), poultry, sheep yard, water bodies, grasslands, built-up areas and roads.

2.2 Sampling and data collection

The study area was divided into 32 square-shaped blocks using 1km × 1km grid for comprehensive sampling, each grid holding one-acre rice fields (farmers field) as sampling locations from following villages: Kalingapeta (L1), Gadalavanipeta (L2), Chittivalasa (L3), Navanambadu (L4), Gollapeta (L5), Kasimvalasa (L6), Bhyrivanipeta (L7), Chevukalupeta (L8), Sriramvalasa (L9), Ponnam (L10), Ponnam (L11), Venkatapuram (L12), Bhyrivanipeta (L13), Ponnam (L14), Ponnam (L15), Challavanipeta (L16), RCRRS Farm (L17), Alikam (L18), Batteru (L19), Naira (L20), Karimillipeta (L21), Karimillipeta (L22), Ronanki (L23), Ronanki (L24), Bhirinagulapeta (L25), Karajada (L26), Singupuram (L27), Karajada (L28), Bhyri (L29), Bhyri (L30), Booravilli (L31) and Booravilli (L32) (Suppl. Table 1). In the 2021, 2022 and 2023 cropping season, rice (cv. MTU 7029, MTU 1061, BPT 5204, MTU 1224 and MTU 1318) planting time ranges from last week of June to the first week of July in study location. Rice crop in sampling locations was grown using local standard agronomic practices, and no insecticidal spray was used in the sampling fields. The average three seasons temperature (August-November) ranged from 23 to 31°C for 32 locations. The sampling locations were installed with two sex pheromone traps each containing lure (Z)-11 hexadecenal + (Z)-9 hexadecenal in a 3:1 ratio, replaced fortnightly procured from Pheromone Chemicals Pvt. Ltd. Hyderabad, India. The YSB months data (count) was collected from sixty-four pheromone traps placed across 32 sampling locations, each visited twelve times (once in August, September, October and November) in 2021, 2022 and 2023 kharif season and at the same time, YSB damage data (number of deadhearts/whiteheads per hill were counted and converted to percent damage) was also collected. The sampling area was divided into landscape elements and YSB trapping/damage points were visualized using QGIS 3.16.4 software (Fig. 1).

2.3 Statistical analysis

The YSB population/damage data was tested for normality, and based on descriptive statistics and Shapiro-Wilk test significance (P < 0.05), the data was transformed (YSB population -square root transformation and YSB damage - arcsine transformation) before statistical analysis. The transformed data was subjected to one-way ANOVA by SPSS statistics software to test the statistical differences between the yearly and monthly YSB population/damage obtained from the sampling area. Tukey’s HSD post hoc test (P = 0.05) was used for multiple comparisons, upon significant difference obtained from the ANOVA. Autocorrelation factor (ACF) plots were generated to observe the autocorrelation of YSB population/damage data at different lags. The YSB population/damage data was decomposed to separate the observed time-series data into trend (long-term progression of the series), seasonal (repeating short-term cycle within the series) and residual (remaining variation in the series after accounting for the trend and seasonal components) components.

To understand the impact of remote sensing data on the YSB population, land surface temperature (LST) and normalized difference vegetation index (NDVI) values obtained from Landsat 8 satellite for 32 locations during the sampling period were correlated with the YSB population/damage using Pearson’s correlation coefficient (P = 0.05). In addition, correlation studies were also conducted between meteorological data (average surface air temperature, average relative humidity and total rainfall) collected from Agriculture College, Naira and YSB population/damage data during the sampling period (from August 2021 to November 2023). A multiple non-linear regression analysis was performed to examine the relationship between YSB population, LST, NDVI, and yield on YSB damage as the dependent variable. Polynomial features of degree 2 were used to account for non-linear effects, and the model was fitted using Ordinary Least Squares (OLS). The model's performance was evaluated based on R-squared and F-statistics.

2.4 Spatial analysis

2.4.1 Spatial distribution

The spatial distribution and dependence of the YSB population and damage on rice were characterized by developing variograms. Variogram plots depict spatial dependence by quantifying the autocorrelation between sampling points, establishing the relationship between sample values and both distance and direction within the sampling area. Variogram(γ) was calculated mathematically as follows (Cressie, 1993; Davis, 1994).

$$\:\widehat{{\gamma\:}\:}\left(h\right)=\:\frac{1}{2N\left(h\right)}\:{\sum\:}_{i=1}^{N\left(h\right)}{\left[z\left({x}_{i}\right)-z({x}_{i}+h)\right]}^{2}$$

Where (γ ) ̂(h) is the estimated semivariance for the entity of interest (z) at all points (x_i ) separated by lag distances (h), and N(h) is the number of pairs of samples separated by lag distance h.

The shape of the variogram plot is characterized by three key parameters: range, sill, and nugget. The 'range' represents the distance within which spatial correlation is present (Liebhold et al. 1993; Fortin and Dale, 2005), while the 'sill' is the semivariance value at which the plot reaches a plateau. The 'nugget' corresponds to the semivariance value at zero lag distance (Liebhold et al. 1993). Linear variogram models typically suggest random distribution patterns, whereas curvilinear models (spherical, gaussian and exponential) indicate aggregation (Journel and Huijbregt, 1978; Isaaks and Srivastava, 1989; Schotzko and O’Keeffe, 1990). In this study, omnidirectional variograms were employed as they provide more precise and apparent results compared to directional variograms (Isaaks and Srivastava, 1989) and have been used in previous studies (Liebhold et al. 1991; Rijal et al. 2014; Seethalam et al. 2021). The degree of YSB population/damage aggregation was assessed using the nugget-to-sill ratio (C₀/C₀ + C), where values below 0.25, between 0.25 and 0.75 and above 0.75 denote strong, moderate and weak aggregation, respectively. The estimation of the nugget-to-sill ratio also provides an amount of randomness that exists in the data at spaces smaller than the sampling distances (Martins et al. 2018; Zhou et al. 2010).

The YSB population/damage data were square root and arcsine transformed, respectively to approximate a normal distribution. Removal of large-scale variation (trend) in the data is a prerequisite for variogram model development (Isaaks and Srivastava, 1989; Cressie, 1993). The sampling month data trend was determined using multiple linear regression analysis using YSB population/damage as a dependent variable and spatial references (coordinates) of individual sampling locations as an independent variable (Park and Tollefso, 2005). Observing the significance of regression (P < 0.05), none of the twelve-month observations used residuals to develop the variogram instead used original values. The variograms were developed in R studio (RStudio, 2019) for monthly YSB counts/damage.

2.4.2 Spatial interpolation and cross-validation

The interpolated maps of the YSB population/damage of the sampling area were produced using ordinary kriging interpolation. Ordinary kriging estimates a value at a specific point within a region, utilizing data from the surrounding area and a known variogram (Wackernagel, 1995). Journel and Deutsch (1993) described ordinary kriging as the anchor algorithm of geostatistics because it shows robustness under different conditions. The kriged estimates maps presented an illustration of the YSB population/damage distribution pattern. Variogram modeling was performed using spherical, gaussian and exponential models, and interpolated files were generated in RStudio (RStudio, 2019). Using QGIS 3.16.4 software the interpolated data were mapped. Geographical shape files for India and Andhra Pradesh state were obtained from the Survey of India, Ministry of Science and Technology, India. The optimal omnidirectional variograms were identified for each interpolation model and month using the coefficient of determination (R²) (Park and Tollefson, 2005; Rijal et al., 2014) and root mean squared error (RMSE) on an absolute scale (Wang et al., 2012). Prediction accuracy was assessed by comparing the mean differences between predicted and observed values, with lower RMSE values indicating greater accuracy.

2.4.3 Spatial autocorrelation and hotspots

In this study, spatial autocorrelation was assessed using Moran’s I to quantify the overall spatial pattern of the YSB population/damage, determining whether clustering or dispersion occurred (Moran, 1950). To further identify specific clusters, Getis-Ord Gi* statistics were applied to detect statistically significant hotspots (high-value clusters) at the local scale (Getis and Ord, 1992). These methods were implemented following standard practices in spatial analysis, providing insight into the spatial dependence and distribution of the data.

3.1 Seasonal Dynamics and Association of Abiotic Factors

In the study area, a total of 5,408 YSB males were attracted to the pheromone traps during the sampling period. Across the locations, the YSB population in rice varied significantly between years (F_96,2 = 135.46, P < 0.05) and months (F_128,3 = 223.96, P < 0.05). Similarly, the damage caused by YSB also differed significantly between years (F_96,2 = 47.50, P < 0.05) and months (F_128,3 = 148.34, P < 0.05) (Fig. 2 and Fig. 3). YSB trap catches and damage symptoms peaked during the early stages of the crop, i.e., tillering (August), and gradually declined from stem elongation to the heading stage (September and October), with a rise again from flowering to the grain-filling stage (November). The seasonal/temporal trends of YSB population and damage were illustrated using time-series plots (Fig. 4).

The decomposition of the time-series data yielded a smoothed long-term trend, seasonal fluctuations, and random/regular noise. The decomposed components are illustrated in Suppl. Figure 1 and Suppl. Figure 2. The trend component demonstrated a long-term upward trend in YSB populations and damage over the sampling period. The seasonal component revealed a recurring pattern of YSB population and damage, with peaks in August and November each year. The residual component captured the random fluctuations not explained by the trend and seasonal components.

The YSB population was significantly associated with the average surface air temperature (r = 0.004), average relative humidity (r = 0.31), or total rainfall (r = 0.042). Similarly, YSB damage was also significantly associated with the average surface air temperature (r = 0.037), average relative humidity (r = 0.257), or total rainfall (r = 0.012). However, remotely sensed data, including LST and NDVI values, showed a significant negative association with the YSB population (r = -0.33; r = − 0.31) and YSB damage (r = − 0.28; r = − 0.34). It is also noticed that YSB population is positively correlated (r = 0.983) with the YSB damage (both deadhearts and whiteheads) during sampling period. Autocorrelation function plots did not show any statistically significant autocorrelation for YSB population and damage data at various lags up to 6 (Suppl. Figure 3).

3.2 Spatial Distribution and Interpolation

The YSB population/damage data from the sampling period were fitted into three variogram models (spherical, gaussian, and exponential). The nugget-sill ratio values were calculated from the variogram parameters to estimate the degree of spatial dependency of YSB population/damage in the sampling area. The three variogram models enumerated a strong aggregation pattern for YSB population and damage in four (August 2021, October 2021, October 2022, August 2023) and five (August 2021, September 2021, October 2021, November 2022, August 2023) sampling months, respectively.

Throughout the sampling period, the spherical model calculated the strongest aggregation of YSB population and damage, while the gaussian and exponential models resulted in all different types of aggregation patterns. The range values calculated from the three theoretical models revealed that the spatial dependency distances (aggregation) of YSB population/damage in rice varied from 1,000 m to 10,630.146 m. The strong spatial autocorrelation was evidenced by the small nugget values (0.00–0.4049; 0.00–0.0071), large sill values (0.0264–4.4189; 0.00–2.9013) and low spatial variance ratios ranging from 0.01 to 0.26 (Suppl. Figure 4 and Suppl. Figure 5).

YSB population/damage interpolation was performed using a 500 m × 500 m grid at unsampled locations by integrating the spatial correlation obtained from the variogram. Spatial distribution maps of YSB population/damage were generated for the sampling locations using predicted values from the variogram models for each sampling month (Fig. 5 and Fig. 6). The interpolated (kriged) values were symbolized using the following parameter settings in QGIS 3.16.4: render type, single band pseudocolor; interpolation, discrete; mode, equal interval; and class, 6. The predicted number of YSB moths trapped or percent damage in each sampling month was mapped using six classes in Fig. 5 and Fig. 6. Higher YSB moth populations were represented in Indian yellow (higher classes), followed by intermediate populations in mustard yellow (middle class) and lower populations in pastel yellow (lower classes). Similarly, higher YSB damage was represented in red (higher classes), followed by intermediate damage in orange (middle classes) and lower damage in chartreuse (lower classes). The variogram models (spherical, Gaussian, and exponential) predicted a low YSB population/damage in October, moderate levels in September, and higher incidences in August and November.

3.3 Spatial Autocorrelation and Hotspots

The YSB population/damage data for each sampling month were organized to create a spatial weights matrix to calculate Moran’s I values. The degree of spatial autocorrelation of YSB population/damage in different sampling months was presented using Moran’s I values: +1 (positive spatial autocorrelation), 0 (no spatial autocorrelation), and − 1 (negative spatial autocorrelation). In all sampling months, Moran’s I values were estimated close to + 1, revealing a significant spatial autocorrelation in the YSB population/damage throughout the crop period, indicating a non-random distribution with clustering of similar values across the study area.

Using the spatial weights matrix, Gi statistics were calculated for each sampling location based on neighbouring values for YSB population/damage data in the study area. Locations with high statistical clusters and z-scores greater than 1.96 were considered hotspots. Getis-Ord Gi* analysis identified statistically significant hotspots at six (L27, L28, L29, L30, L31, L32) and nine (L16, L18, L26, L27, L28, L29, L30, L31, L32) locations where high YSB population and damage concentrations were observed, respectively (Suppl. Table 4).

Understanding the factors affecting the spatial distribution/pattern of YSB in fragmented rice landscapes is an essential component of population ecology. It is vital for predicting YSB populations, enhancing sampling techniques and applying this knowledge to decision-making in pest management (Haila et al., 2002; Binns et al., 2000; Yang et al., 2018; Ribeiro et al., 2021). In this study, YSB moth populations and damage followed a ‘peak-fall-rise’ pattern in rice crops during the kharif season across the sampling locations (Fig. 4). An intraseasonal rise and fall of YSB populations and damage in rice have been previously reported (Kumar, 1995; Justin and Preetha, 2013; Patel and Singh, 2017; Baskaran, 2019; Jasrotia et al., 2019; Bapatla et al., 2024). Pathak (1968) opined that peaks in YSB populations reflect major seasonal effects, which have been misinterpreted as different broods. The principal factors -abiotic (surface air temperature, rainfall, day length, and humidity) and host plant phenology (Pathak, 1968) - define YSB population dynamics and damage in the study area. The occurrence of a warm and humid climate (Pathak, 1968; Adiroubane and Raja, 2010) and the excessive application of nitrogen fertilizer (Hirano, 1964; Singh and Sarkar, 2021) encouraged YSB populations and damage during the vegetative stage of the crop in August and September. Subsequently, the initiation of low surface air temperatures around 28°C (Kiritani and Iwao, 1967) and short-day lengths of 8–14 hours (Inoue and Kamano, 1957) in October induced diapause in YSB. In the last week of October, the availability of the susceptible stage of rice (panicle emergence) and rainfall terminated the diapause (Van der Laan, 1959), resulting in a buildup of YSB populations and damage in November. Our findings are corroborated by earlier studies on peak activity periods of YSB populations and damage during July, August and September (Varma et al., 2000), August and September (Justin and Preetha, 2013; Jasrotia et al., 2019), and September (Patel and Singh, 2017) in the kharif season.

A strong and positive association between surface air temperature and relative humidity with YSB populations and damage was proven in this study. This is further substantiated by the observation that high temperatures coupled with high relative humidity (Kumar and Sudhakar, 2001; Mishra et al., 2005; Hugar et al., 2010) in August and September, and low temperatures in November (Rahaman et al., 2014), favoured YSB population buildups and led to severe damage in rice. Although no previous studies have explained the relationship between LST and NDVI values with YSB populations or damage in rice, our study attempted to do so. We observed a statistically negative relationship between them, but the factors responsible for this association are not fully understood yet, and potential causes should be explored in future studies.

In addition to biotic, abiotic and host factors, a spatial dimension was introduced in this study to characterize YSB populations and their damage in rice. Temporal variations in YSB in rice have been reported using pheromone and light trap catches (Kumar, 1995; Baskaran et al., 2019; Gangappa et al., 2023; Bapatla et al., 2024) and damage assessments (Justin and Preetha, 2013; Patel and Singh, 2017; Jasrotia et al., 2019; Sharma and Sharma, 2023), but spatial points were not considered in earlier studies. To understand the effect of space on YSB, a semivariogram was developed through geostatistical analysis. Spatial aggregation of YSB populations and damage was detected using the semivariogram in all 12 sampling months, indicating weak to strong temporal aggregation across the rice fields in the study area (Suppl. Figure 4 and Suppl. Figure 5). Based on the spatial variance ratio, strong spatial aggregation/dependence was detected in four and five sampling months for YSB populations and damage, respectively. Although geostatistical tools have been utilized to understand the spatiotemporal distribution of several agricultural pests such as Helicoverpa armigera in pigeonpea (Seethalam et al., 2021), Plutella xylostella in crucifers (Li et al., 2021), Dalbulus maidis in corn (Ribeiro et al., 2021) and Chrysolina aeruginosa in desert vegetation (He et al., 2023), this study is the first of its kind to document the spatial distribution of YSB populations and their damage in rice.

In randomly sampled rice fields, visual-counting methods like pheromone trap catches and percent-damaged tillers (deadhearts and whiteheads) are the most common methods for YSB population monitoring and damage assessment (Jasrotia et al., 2019; Varma et al., 2000). However, these methods are laborious and time-consuming, especially in larger rice fields. Therefore, to develop reliable sampling methods for insect pest monitoring and population assessment, variogram models (spherical, Gaussian, and exponential) were used to estimate the range values that specify the aggregated distribution pattern (Shrestha et al., 2020; Seethalam et al., 2021). The range values also signify the end of spatial autocorrelation of YSB populations and damage in the space dimension (experimental area). Our findings suggest that an average sampling distance (range value) of 584.1 m and 673.33 m should be followed to reduce scouting fatigue for counting YSB pheromone trap catches and percent damage in large rice fields for monitoring purpose. These results may improve scouting and monitoring of YSB populations and damage in rice.

The results of Moran’s I indicate significant spatial clustering of YSB, suggesting that the population and damage are not randomly distributed but exhibit spatial dependence, which aligns with similar findings in spatial ecology (Li et al., 2024). The identification of YSB population/damage hotspots using the Getis-Ord Gi* statistic further emphasizes that the southeast direction of the sampling area (L27, L28, L29, L30, L31, and L32) had more hotspots, which may be influenced by natural and socio-economic factors (Li et al., 2024). These findings are crucial for targeted pest control strategies, as focusing on identified hotspots can optimize resource allocation and enhance control efficiency. The propensity of insect movement in patches and settlement in space in a contagious fashion, within or between habitats, tends to be directional and not random (Stinner et al., 1983). This spatial pattern of immigration of YSB, prior to colonizing and increasing in population size, can be managed by following “precision targeting” the hotspots identified in this study. Thus, one of the possible implications of this study for managing YSB is that the early installation of a reasonable number of sex pheromone traps, along with the placement of bioagent trichocards(containing Trichogramma japonicum parasitized eggs of Corcyra cephalonica) in hotspot locations, can further prevent the movement of YSB. In this way, the use of pesticides can be minimized.

This study highlights the significance of understanding the spatiotemporal distribution of the rice YSB in coastal rice ecosystems of Andhra Pradesh, India. Seasonal fluctuations in YSB populations were driven by abiotic factors such as temperature, humidity, and rainfall. The geostatistical analysis revealed strong spatial dependence in YSB population and damage, indicating aggregation patterns across the landscape. Spatial interpolation and hotspot analysis identified regions with higher pest concentrations, allowing for precision-targeted pest management strategies. These findings provide a foundation for improving pest monitoring, optimizing control efforts and reducing environmental impacts in rice cultivation.

Funding Declaration : No fund is received to conduct this study.

Author Contribution

KGB and CBN conceived and designed the concept. KGB and UBP collected and curated the data. KGB and SY analysed the data. KGB, SCS, GB and KKR wrote the manuscript. KGB, CBN, UBP, SY and BBP edited the manuscript. All authors approved the manuscript.

Acknowledgement

Authors are grateful to ICAR-National Rice Research Institute for providing the facilities and the services of the pest scouts engaged under the study.

Data Availability

Data is provided within the manuscript or supplementary information files

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No competing interests reported.

Suppl.Table1.docx
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Supplementary Figure 1: Trends and seasonality of yellow stem borer adult moths over the sampling period in the study area.
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Supplementary Figure 2: Trends and seasonality of yellow stem borer damage over the sampling period in the study area.
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Supplementary Figure 3: Estimation of the autocorrelation coefficient for yellow stem borer trap catch and damage data.
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Supplementary Figure 4: Semivariogram curves of the spatial patterns of yellow stem borer trap catches in the study area.
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Supplementary Figure 5: Semivariogram curves of the spatial patterns of yellow stem borer damage in the study area.

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Exploring the spatial and temporal dimension of yellow stem borer in coastal rice ecosystem for enhanced management using geostatistical analysis

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Abstract

Figures

1. Introduction

2. Material and Methods

2.1 Study location

2.2 Sampling and data collection

2.3 Statistical analysis

2.4 Spatial analysis

2.4.1 Spatial distribution

2.4.2 Spatial interpolation and cross-validation

2.4.3 Spatial autocorrelation and hotspots

3. Results

3.1 Seasonal Dynamics and Association of Abiotic Factors

3.2 Spatial Distribution and Interpolation

3.3 Spatial Autocorrelation and Hotspots

4. Discussion

5. Conclusion

Declarations

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Supplementary Files

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