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

Growth and yield of maize (Zea mays L.) as influenced by date of sowing and hybrids

A field experiment was conducted at college of Agriculture, UAHS, Shivamogga during kharif 2015 to study the effect of date of sowing and hybrids on growth and yield of Maize (Zea mays L.). The experi-ment was laid out in randomized complete block design (RCBD) with factorial concept and replicated thrice. There were eight treatment combinations which includes four dates of sowing (15th June, 30th June, 15th July and 30th July) and two hybrids (PAC-740 and CP-818). Crop sown on 15th June recorded significantly higher plant height (201.03 cm), number of green leaves (3.03), leaf area (992.49 cm2), LAI (0.74), total dry matter (305.65 g), cob length (22.16 cm), kernels cob-1 (670.93), kernel yield cob-1 (230.95 g), test weight (43.08 g), kernel yield (7632.57 kg ha-1), stover yield (9512.56 kg ha-1) and har-vest index (44.52 %) as compared to other sowing dates. Among the hybrids CP -818 recorded significantly higher plant height (191.85 cm), number of green leaves (2.72), leaf area (954.32 cm2), LAI (0.71), total dry matter (277.65 g), cob length (19.81 cm), kernels cob-1 (541.88), kernel yield cob-1 (207.71 g), test weight (39.16 g), kernel yield (7060.72 kg ha-1), Stover yield (8839.98 kg ha-1) and harvest in-dex (44.44%) as compared to PAC-740. The interaction between dates of sowing and hybrids are non-significant

Multistage sugarcane yield prediction using machine learning algorithms

Sugarcane is one of the leading commercial crops grown in India. The prevailing weather during the various crop-growth stages significantly impacts sugarcane productivity and the quality of its juice. The objective of this study was to predict the yield of sugarcane during different growth periods using machine learning techniques viz., random forest (RF), support vector machine (SVM), stepwise multiple linear regression (SMLR) and artificial neural networks (ANN). The performance of different yield forecasting models was assessed based on the coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (nRMSE) and model efficiency (EF). Among the models, ANN model was able to predict the yield at different growth stages with higher R2 and lower nRMSE during both calibration and validation. The performance of models across the forecasts was ranked based on the model efficiency as ANN > RF > SVM > SMLR. This study demonstrated that the ANN model can be used for reliable yield forecasting of sugarcane at different growth stages

Biomass Quantity and Quality from Different Year-Round Cereal–Legume Cropping Systems as Forage or Fodder for Livestock

The quantity and quality of forage and fodder crops is the major drawback of the livestock sector in the country. There is a need to bridge the gap between the supply and demand of fodder through the adoption of specific sustainable fodder production strategies. The field experiments were conducted during kharif (rainy, June–October), rabi (post-rainy, October–February), and summer (March–May) seasons of 2018–19 and 2019–20 to identify a sustainable fodder cropping system module in randomized complete block design with fifteen fodder cropping systems in three replications. The main objective of this research was to identify the most productive cereal–legume cropping system, both in terms of quantity and quality of biomass, to reduce the gap between supply and demand of quality livestock feed around the year. Among cropping systems, Bajra–Napier hybrid intercropped with lucerne, cowpea, and sesbania recorded significantly higher green fodder (163.6, 155.2, and 144.0 t/ha/year, respectively) and dry matter yields (32.1, 30.8, and 31.3 t/ha/year, respectively). Similarly, the same perennial systems also recorded higher quality yield and ash content. However, higher crude protein content was noticed in monocrop legumes, with the highest in sesbania (22.32%), while higher ether extractable fat was found in monocrop sesbania (3.78%). The monocrop oats recorded higher non-fiber carbohydrates (36.90%) while a monocrop of pearl millet recorded higher total carbohydrates (80.75%), however they were on par with other monocrop cereal cropping systems. Cultivation of legumes as a monocrop, and their inclusion as an intercrop with cereals resulted in lower fiber fractions and improved crude protein in intercropping systems. Furthermore, this improved the dry matter intake and digestibility of fodder. With higher sustainable yield index values and land-use efficiency, perennial intercropping systems were also found to be sustainable. Thus, cultivation of the Bajra–Napier hybrid with either lucerne, cowpea, or sesbania as an intercrop will help livestock farmers to achieve higher productivity in terms of quantity and quality, and forms a viable option for overcoming livestock feed scarcity

Molecular Characterization and Genetic Variation in Ceratocystis&nbsp;fimbriata Ell. and Halst. on Pomegranate

Fifteen isolates of Ceratocystis&nbsp;fimbriata collected from different locations in Karnataka were characterized using ITS gene technology. It produced an amplification size of 600&ndash;650 bp, which indicated that all the isolates belong to the genus Ceratocystis, thus confirming the identity of the pathogenic isolates. To test genetic variability, isolates were analyzed using microsatellite markers. An UPGMA dendrogram for genetic variation among the isolates showed that all the isolates fell into two major clusters. The first cluster consisted of isolate Cf-10 and the second cluster was further divided into two sub-clusters. Sub-cluster one consisted of isolate Cf-2. Sub-cluster two was again divided into five groups. The first group included isolate Cf-13, the second group consisted of isolate Cf-14, the third group included isolates Cf-1, Cf-4, Cf-6, Cf-7, Cf-8 and Cf-9, the fourth group included Cf-5 and Cf-11, and the fifth group consisted of Cf-3, Cf-12 and Cf-15. The dissimilarity coefficient ranged from 0.00 to 0.20 among the isolates. Isolates Cf-1, Cf-3, Cf-4, Cf-5 Cf-6, Cf-7, Cf-8, Cf-9, Cf-11, Cf-12 and Cf-15 were found to be highly similar, as their dissimilarity coefficient was zero. Maximum dissimilarity (0.20) was found between isolate Cf-10 and all the other isolates, suggesting they were genetically distinct

Assessment of the Spatial Distribution and Risk Associated with Fruit Rot Disease in Areca catechu L.

Phytophthora meadii (McRae) is a hemibiotrophic oomycete fungus that infects tender nuts, growing buds, and crown regions, resulting in fruit, bud, and crown rot diseases in arecanut (Areca catechu L.), respectively. Among them, fruit rot disease (FRD) causes serious economic losses that are borne by the growers, making it the greatest yield-limiting factor in arecanut crops. FRD has been known to occur in traditional growing areas since 1910, particularly in Malnad and coastal tracts of Karnataka. Systemic surveys were conducted on the disease several decades ago. The design of appropriate management approaches to curtail the impacts of the disease requires information on the spatial distribution of the risks posed by the disease. In this study, we used exploratory survey data to determine areas that are most at risk. Point pattern (spatial autocorrelation and Ripley’s K function) analyses confirmed the existence of moderate clustering across sampling points and optimized hotspots of FRD were determined. Geospatial techniques such as inverse distance weighting (IDW), ordinary kriging (OK), and indicator kriging (IK) were performed to predict the percent severity rates at unsampled sites. IDW and OK generated identical maps, whereby the FRD severity rates were higher in areas adjacent to the Western Ghats and the seashore. Additionally, IK was used to identify both disease-prone and disease-free areas in Karnataka. After fitting the semivariograms with different models, the exponential model showed the best fit with the semivariogram. Using this model information, OK and IK maps were generated. The identified FRD risk areas in our study, which showed higher disease probability rates (&gt;20%) exceeding the threshold level, need to be monitored with the utmost care to contain and reduce the further spread of the disease in Karnataka

Assessment of the spatial distribution and risk associated with fruit rot disease in Areca catechu L.

Phytophthora meadii (McRae) is a hemibiotrophic oomycete fungus that infects tender nuts, growing buds, and crown regions, resulting in fruit, bud, and crown rot diseases in arecanut (Areca catechu L.), respectively. Among them, fruit rot disease (FRD) causes serious economic losses that are borne by the growers, making it the greatest yield-limiting factor in arecanut crops. FRD has been known to occur in traditional growing areas since 1910, particularly in Malnad and coastal tracts of Karnataka. Systemic surveys were conducted on the disease several decades ago. The design of appropriate management approaches to curtail the impacts of the disease requires information on the spatial distribution of the risks posed by the disease. In this study, we used exploratory survey data to determine areas that are most at risk. Point pattern (spatial autocorrelation and Ripley’s K function) analyses confirmed the existence of moderate clustering across sampling points and optimized hotspots of FRD were determined. Geospatial techniques such as inverse distance weighting (IDW), ordinary kriging (OK), and indicator kriging (IK) were performed to predict the percent severity rates at unsampled sites. IDW and OK generated identical maps, whereby the FRD severity rates were higher in areas adjacent to the Western Ghats and the seashore. Additionally, IK was used to identify both disease-prone and disease-free areas in Karnataka. After fitting the semivariograms with different models, the exponential model showed the best fit with the semivariogram. Using this model information, OK and IK maps were generated. The identified FRD risk areas in our study, which showed higher disease probability rates (>20%) exceeding the threshold level, need to be monitored with the utmost care to contain and reduce the further spread of the disease in Karnataka

Identification of Sustainable Development Priorities for Agriculture through Sustainable Livelihood Security Indicators for Karnataka, India

To cope with worsening climate change and widening intergenerational equity issues, more impetus should be given to sustainable development. India, predominantly an agrarian economy, faces most pressing issues of sustainable development with a complex territorial hegemony of the population and their dynamic food demands. Regional production systems play a vital role in strengthening national sustainable development priorities in India. Hence, to realize the dimensions of sustainable development in a more meaningful way, sustainability needs to be prioritized in an agrarian economy. Sustainability is a complex phenomenon encompassing economic, ecological and equity dimensions. A modest attempt in this regard has been made to estimate normative sustainable indicators for Karnataka state considering 20 crucial indicators or variables governing different dimensions. Using principal component analysis and linear scoring techniques, a minimum dataset including forest cover, livestock and human population density, and cropping intensity governing ecological issues, groundwater availability and milk availability governing social equity issues, and net cropped area, land productivity, labor productivity, food grain productivity and fertilizer use governing economic efficiency was identified, constituting crucial indicators for the development of the sustainable livelihood security index. The computed index was used to classify districts in Karnataka into various sustainable categories. Among 27 districts, 13 districts were grouped as less sustainable, 4 as highly sustainable and 10 as moderately sustainable categories. This classification and knowledge provide clues for policy makers to transform less sustainable districts into moderately/highly sustainable ones by formulating suitable policies related to crucial factors. Formulated policies on crucial factors have a domino effect/causation effect and bring about desirable changes in all other indicator variables, leading to the sustainable development of the target districts in Karnataka. This approach can be used at different scales in other states in India and in other developing countries