Trend analysis and forecasting coconut production in Assam

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Trend analysis and forecasting coconut production in Assam

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Forecasting of arecanut market price in north eastern India: ARIMA modelling approach

The paper deals with forecasting of minimum, maximum and average arecanut (Areca catechu L.) prices in the major arecanut markets of the Assam as well as Meghalaya based on the monthly price data. Monthly minimum, maximum, and average market price data of arecanut (in Rs./quintal) for the period May-2003 to March-2012 (for Assam) and February-2003 to March-2012 (for Meghalaya) were used. Box-Jenkins autoregressive integrated moving average (ARIMA) methodology was adopted for developing the models. An interrupted time-series model was also applied to resolve the problem of intervention point (October-2011) for Meghalaya price data. The proposed models were ARIMA (1, 0, 1), ARIMA (1, 1, 1), ARIMA (0, 1, 1) (for Assam market price data series) and, log ARIMA (0, 1, 1), log ARIMA (1, 0, 1) with linear trend and a man-made intervention (Oct-2011) and log ARIMA (0, 1, 1) with linear trend and a manmade intervention (Oct-2011) (for Meghalaya market price data series) for minimum, maximum, and average monthly price series, respectively

Forecasting of arecanut market price in north eastern India: ARIMA modelling approach

The paper deals with forecasting of minimum, maximum and average arecanut (Areca catechu L.) prices in the major arecanut markets of the Assam as well as Meghalaya based on the monthly price data. Monthly minimum, maximum, and average market price data of arecanut (in Rs./quintal) for the period May-2003 to March-2012 (for Assam) and February-2003 to March-2012 (for Meghalaya) were used. Box-Jenkins autoregressive integrated moving average (ARIMA) methodology was adopted for developing the models. An interrupted time-series model was also applied to resolve the problem of intervention point (October-2011) for Meghalaya price data. The proposed models were ARIMA (1, 0, 1), ARIMA (1, 1, 1), ARIMA (0, 1, 1) (for Assam market price data series) and, log ARIMA (0, 1, 1), log ARIMA (1, 0, 1) with linear trend and a man-made intervention (Oct-2011) and log ARIMA (0, 1, 1) with linear trend and a manmade intervention (Oct-2011) (for Meghalaya market price data series) for minimum, maximum, and average monthly price series, respectively

Climatic Determinants of Shrimp Yields in Tamil Nadu, India: A Transfer Function Analysis

Shrimp aquaculture in Tamil Nadu plays a critical role in supporting livelihoods and contributing to exports but is increasingly vulnerable to climatic variability. This study investigates how seasonal precipitation, extreme temperatures, and lagged production impact shrimp yields in four key districts—Tiruvallur, Cuddalore, Thanjavur, and Nagapattinam—over the period 2015–2023. Using statistical models, we identify major trends and district-specific sensitivities. For instance, excessive summer and winter precipitation coupled with high maximum temperatures reduced yields in Tiruvallur, whereas warmer summer nights boosted production. In Cuddalore, yields were hampered by seasonal precipitation, elevated minimum temperatures, and the lingering effects of previous yields. Thanjavur’s production suffered from extreme temperatures and winter rainfall but benefited from milder winter nights. Nagapattinam showed unique dynamics, where winter precipitation and summer maximum temperatures enhanced yields, while summer rainfall and lagged production had adverse effects. These findings highlight the need for tailored, district-specific strategies to manage climate risks and sustain shrimp farming. For example, adaptive measures such as optimizing water management or selecting climate-resilient shrimp varieties could mitigate negative impacts. Future research could integrate factors like water quality and disease outbreaks to strengthen the sector’s resilience further

PolyMorphPredict: A Universal Web-Tool for Rapid Polymorphic Microsatellite Marker Discovery From Whole Genome and Transcriptome Data

Microsatellites are ubiquitously distributed, polymorphic repeat sequence valuable for association, selection, population structure and identification. They can be mined by genomic library, probe hybridization and sequencing of selected clones. Such approach has many limitations like biased hybridization and selection of larger repeats. In silico mining of polymorphic markers using data of various genotypes can be rapid and economical. Available tools lack in some or other aspects like: targeted user defined primer generation, polymorphism discovery using multiple sequence, size and number limits of input sequence, no option for primer generation and e-PCR evaluation, transferability, lack of complete automation and user-friendliness. They also lack the provision to evaluate published primers in e-PCR mode to generate additional allelic data using re-sequenced data of various genotypes for judicious utilization of previously generated data. We developed the tool (PolyMorphPredict) using Perl, R, Java and launched at Apache which is available at http://webtom.cabgrid.res.in/polypred/. It mines microsatellite loci and computes primers from genome/transcriptome data of any species. It can perform e-PCR using published primers for polymorphism discovery and across species transferability of microsatellite loci. Present tool has been evaluated using five species of different genome size having 21 genotypes. Though server is equipped with genomic data of three species for test run with gel simulation, but can be used for any species. Further, polymorphism predictability has been validated using in silico and in vitro PCR of four rice genotypes. This tool can accelerate the in silico microsatellite polymorphism discovery in re-sequencing projects of any species of plant and animal for their diversity estimation along with variety/breed identification, population structure, MAS, QTL and gene discovery, traceability, parentage testing, fungal diagnostics and genome finishing

Spatial Distribution Patterns for Identifying Risk Areas Associated with False Smut Disease of Rice in Southern India

False smut disease (FSD) of rice incited by Ustilaginoidea virens is an emerging threat to paddy cultivation worldwide. We investigated the spatial distribution of FSD in different paddy ecosystems of South Indian states, viz., Andhra Pradesh, Karnataka, Tamil Nadu, and Telangana, by considering the exploratory data from 111 sampling sites. Point pattern and surface interpolation analyses were carried out to identify the spatial patterns of FSD across the studied areas. The spatial clusters of FSD were confirmed by employing spatial autocorrelation and Ripley’s K function. Further, ordinary kriging (OK), indicator kriging (IK), and inverse distance weighting (IDW) were used to create spatial maps by predicting the values at unvisited locations. The agglomerative hierarchical cluster analysis using the average linkage method identified four main clusters of FSD. From the Local Moran’s I statistic, most of the areas of Andhra Pradesh and Tamil Nadu were clustered together (at I > 0), except the coastal and interior districts of Karnataka (at I < 0). Spatial patterns of FSD severity were determined by semi-variogram experimental models, and the spherical model was the best fit. Results from the interpolation technique, the potential FSD hot spots/risk areas were majorly identified in Tamil Nadu and a few traditional rice-growing ecosystems of Northern Karnataka. This is the first intensive study that attempted to understand the spatial patterns of FSD using geostatistical approaches in India. The findings from this study would help in setting up ecosystem-specific management strategies to reduce the spread of FSD in India

Timing of oomycete-specific fungicide application impacts the efficacy against fruit rot disease in arecanut

Fungicidal application has been the common and prime option to combat fruit rot disease (FRD) of arecanut (Areca catechu L.) under field conditions. However, the existence of virulent pathotypes, rapid spreading ability, and improper time of fungicide application has become a serious challenge. In the present investigation, we assessed the efficacy of oomycete-specific fungicides under two approaches: (i) three fixed timings of fungicidal applications, i.e., pre-, mid-, and post-monsoon periods (EXPT1), and (ii) predefined different fruit stages, i.e., button, marble, and premature stages (EXPT2). Fungicidal efficacy in managing FRD was determined from evaluations of FRD severity, FRD incidence, and cumulative fallen nut rate (CFNR) by employing generalized linear mixed models (GLMMs). In EXPT1, all the tested fungicides reduced FRD disease levels by &gt;65% when applied at pre- or mid-monsoon compared with untreated control, with statistical differences among fungicides and timings of application relative to infection. In EXPT2, the efficacy of fungicides was comparatively reduced when applied at predefined fruit/nut stages, with statistically non-significant differences among tested fungicides and fruit stages. A comprehensive analysis of both experiments recommends that the fungicidal application can be performed before the onset of monsoon for effective management of arecanut FRD. In conclusion, the timing of fungicidal application based on the monsoon period provides better control of FRD of arecanut than an application based on the developmental stages of fruit under field conditions

Assessment of the spatial distribution and identification of potential risk areas for the sterility mosaic disease of pigeonpea (Cajanus cajan L. Huth) in Southern India

Sterility mosaic disease (SMD) is an emerging biotic risk to the cultivation of pigeonpea throughout the Indian subcontinent. The prevalence and distribution of SMD documented from diverse agro-climatic zones are still poorly known. In this investigation, we determined the spatial distribution of SMD covering major pigeonpea-growing states of Southern India namely, Karnataka, Telangana, Tamil Nadu, Maharashtra, and Andhra Pradesh by considering 2 years of exploratory survey data. Geospatial approaches were employed to determine the SMD incidence levels at unvisited locations using neighboring observations. The results indicated enormous variability in disease incidence ranging from 0 to 82.64% and 0 to 36.84%, respectively, in Kharif 2021–22 and 2022–23 in pigeonpea-growing states. Spatial autocorrelation analysis and Ripley’s K function established the presence of average clustering over sampling locations. Two main clusters were identified via agglomerative hierarchical cluster analysis. From the Local Moran’s I spatial autocorrelation (LISA) analysis, most of the districts were clustered together at (I&lt;0). From the p-values, Bidar and Krishnagiri districts recorded the highest spatial dependence, while the remaining districts recorded medium spatial dependence (p &lt; 0.05). Based on kriging, the Chittoor district of Andhra Pradesh, Medak and Ranga Reddy districts of Telangana, Bijapur district of Karnataka, and Latur district in Maharashtra states were found to be vulnerable to SMD occurrence in the future. The Matern and Spherical models were shown to be the best of the several semivariogram experimental models tested for spatial patterns of SMD incidence, from which OK and IK maps were created. Based on the interpolation results, the potential SMD hotspots/risk areas were most prominent in Telangana, Andhra Pradesh, Karnataka, and Tamil Nadu states. These identified hotspots for SMD revealed high levels of disease probability rates (&gt;25%) above the threshold level and must be closely monitored to restrict and minimize further disease spread across the studied areas in Southern India. In pigeonpea, this is the first study in India that attempted to understand the spatial pattern of SMD using geostatistical methodologies. The results of the study will aid in the design and dissemination of specific management practices and curb the further spread of SMD