Search

Forecasting fish landings is a critical element tool for fisheries managers and policymakers to short-term quantitative recommendations for fisheries management. In this study, the forecasting of a quarterly landing of total fish catch and the catch of major pelagic fish species (Indian Mackerel and Bombay duck) was done by nonlinear autoregressive with exogenous inputs (NARX), an artificial neural network model. The quarterly landings data of total fish catch and the catch of major pelagic fish along with quarterly average data on the mean value of environmental variables were used for building the model and forecasting. The developed NARX model was validated with the actual fish catch on holdout data with prediction error 2.45–11.42%. Further, the developed NARX model was used to forecast fish catch for the next 20 quarters (5 years) and was compared and found good agreement with the actual catch reported by Central Marine Fisheries Research Institute, Kochi, annual report(Year- 2014, 2015 and 2016). The developed NARX model in the present case study is of the first time to forecast the fish catch landing using exogenous input in the Maharashtra region.

This may be the first attempt to correlate environmental parameters and soil ecology impacting disease situation in Nagpur mandarin due to Phytophthora. Fixed plot survey was undertaken at fortnightly intervals at six locations to analyse correlation between environmental factors and soil moisture with propagule density of Phytophthoraas well as disease severity by using linear correlation. Rainfall, relative humidity and air temperature probes typically consist of three separate sensors packaged in a single instrument called as ‘SENSTUBE’ and that instrument was used for recording the data in field. Soil moisture was analysed in laboratory by using digital moisture meter. Fixed plot study indicated inoculum density of Phytophthorain the range of 5.2–29.6 cfu/g soil. Peak period of Phytophthoraroot rot in Nagpur mandarin was seen from June to September and mean disease intensity recorded in between 3.16 and 34.33%. Root rot in Nagpur mandarin gradually increased after heavy rainfall and progressed with faster rate when soil moisture with high humidity and low temperature and vice versa. Fortnightly study of gummosis disease intensity was in the range of 4.08–30.17%. Peak period of gummosis disease (August to November) was noted after one to two months of heavy rainfall having optimum temperature (below 30 °C) and high humidity. After heavy rainfall the pathogen population is drastically increased but oozing of gum was observed after monsoon. Phytophthorapopulation and disease progression was directly proportional to environmental and soil parameters like rainfall, relative humidity, soil moisture, but inversely proportional to air temperature. This study will be helpful to the citrus growers to predict the timing of disease severity to take appropriate management practices in time.

Fixed plot survey was undertaken to plot correlation between environmental factors with population of Phytophthoraspp. in soil and the disease intensity during June 2015 to May 2016. Agro-meteorological data was recorded by using wireless sensors (for air temperature 107 Temperature probe, for relative humidity HC2S3 and for rainfall measurement Texas Electronic rain gauge) in selected plots. All four orchards were found infected with Phytophthorain the range of 3.2–43.6 cfu/g of soil. There was consistent increase in pathogen population in all plots with increase in rainfall, relative humidity with decrease in temperature and vice versa. At high rainfall condition intensity of root rot was increased to 35.81 per cent in the month of August and gummosis (41.01%) was also increased in the month of October with average propagules of 25.84 cfu/g of soil. Increase in temperature decreased intensity of gummosis (26.29%) and root rot (20.09%) in the month of May, with the average propagules, i.e., 4.56 cfu/g of soil. Present findings suggest that, humidity and rainfall were significantly correlates with population dynamics of Phytophthoraspp. and disease intensity, while temperature was found inversely proportional.

Information on existing landuse pattern and its spatial distribution is a pre-requisite for any watershed management programme. With the advent of remote sensing tools, with their inherent characteristics, it has been possible to prepare this dynamic resource map at various levels of confidence. But the application of multi-spectral classification techniques in vegetation discrimination has been the subject of discussion for many years.The paper describes a methodology developed to compile agricultural landuse map of a hilly watershed (where the rainfall erosivity is an ever-present threat) using geographical information systems (GIS). Multi-seasonal (monsoon - Kharif and post-monsoon - Rabi) and multi-sensor remotely-sensed data were used for mapping a landuse pattern of the watershed. The rasterised classified images, and the relevant watershed resources, were input and stored as separate layers in the GIS and then geometrically co-registered to a regular 30 m grid. Knowledge-based rules were developed from the informed opinion of multi-disciplinary experts and field checkings, in addition to the knowledge of local landuse patterns. These expert rules were used to manipulate the information databases to discriminate various spectrally inseparable information classes, finding out the landuse/cover categories of the Kharif season under the cloud and its shadow areas. Finally, the improved and the agricultural landuse pattern map was classified into thick forest, sparse forest, degraded pastures, open (with/without scrub) areas, cropped (Kharif + Rabi) and fallow (Kharif + Rabi) lands. The areal extent of improved/final landuse map classes compared favourably with the natural conditions of the agro-climatic region of the watershed. The paper also envisages some future studies for watershed management policies.