P.K. Aggarwal

Impacts of Climate Change on Agriculture

Climate change has emerged as the most prominent of the global environment issues and there is a need to evaluate its impact on agriculture. Crop simulation models help greatly in this regard. Crop models such as WTGROWS, INFOCROP, ORYZA and DSSAT have been widely used for land use planning, agri-production estimates, impact of climate change and environmental impact analysis. Vulnerable regions under future scenarios of climate change and adaptation strategies (agronomic and input management) have been evolved for many important crops by using simulation techniques. One of the simple empirical techniques for evaluating the impact of future climate change is through historic analysis of the response of crops to inter-seasonal climatic variability. The impact of temperature rise is different for crops grown under variable production environments. Interactions exist for changes in temperature, carbon dioxide concentration, solar radiation and rainfall on growth and yield of crops. Adaptation strategies through the adoption of agronomic management options (such as altered date of sowing, scheduling of water and nutrients) can sustain agricultural productivity under climate change. The rapid changes in land use and land cover have to be included for impact analysis. Linking of the socioeconomic aspects needs to be strengthened.

Soil physical quality as influenced by long-term application of fertilizers and manure under maize-wheat system.

Soil physical environment as affected by long-term fertilizer experiment application in a maize-wheat system on sandy loam soils of India was characterized and quantified using a unified soil physical quality index (S). Treatments were 100% and 150% of recommended nitrogen, phosphorus, and potassium (NPK); 100% NPK + farmyard manure; 100% NPK + sulfur; and control (no fertilizer or manure). Soil aggregation, bulk density, organic carbon in bulk soil (SOC) and aggregates, pore-size distribution, saturated hydraulic conductivity, field capacity moisture content, and plant-available water content were evaluated. Most of the effects were pronounced in 0- to 15-cm layer. Better aggregation was found with 100% NPK + farmyard manure, where macroaggregates were greater than 50% of total soil mass. Aggregation indices were positively and significantly correlated with SOC in 8- to 4-mm aggregates. Bulk density was significantly lower (1.51 Mg m−3) with manure, corresponding to maximum SOC content (6.8 g kg−1). The field capacity moisture content, plant-available water content, and saturated hydraulic conductivity were significantly higher in manure plots. Transmission and storage pores were more abundant in manure-treated plots. Effects of 150% NPK or 100% NPK + sulfur doses was better compared with 100% NPK, indicating that the recommended dose of NPK was suboptimal to maintain the desired soil physical health. Close associations of S with soil physical parameters was obtained, indicating potential of S in quantifying the modifications of soil physical environment through fertilizer and manure applications.

Development and Application of Crop Growth Simulation Modelling in Pest Management

Crop growth simulation models based on crop ecological and physiological processes, and coupled with pest damage mechanisms, are useful tools for analysing the interaction of pests with other biophysical components of agroecosystems. The use of simulation models in pest management started with the development of single-species population dynamics models in the late 1960s. Later, population dynamics models for bitrophic interactions between pests and natural enemies were devised. Crop growth simulation models were coupled with pest damage mechanisms in the mid-80s, but the approach was described as a one-way analysis of crop–pest interactions because these models accounted for pest effects on crop growth without the reverse being considered. A two-way approach for crop–pest interactions came into being with the interlinking of pest population dynamics models with crop growth simulation models in the 1990s. Pest management research is required to produce practical tools for developing tactics and strategies for pest management. Simulation models help to produce such tools and decision models. They are used for various applications, such as rationalizing pesticide use, pest risk analysis, pest zonation, analysis of climate change impact on pests, and assessing the effect of transgenic crops on the environment. These models have the potential to play even greater roles in the future.

Determinants of Corporate Profitability: An Empirical Study of Indian Drugs and Pharmaceutical Industry

This paper provides on empirical evidence about the determinants of profitability of selected companies in drugs and pharmaceutical industry in Indio. It is based on a sample of fifty firms in drugs and pharmaceutical industry drawn from PROWESS database developed by CMIE. It covers a period of ten years from 1995-6 to 2004-5. The profitability of firms has been measured in terms of overage return on capital employed. In order to study the determinants of profitability, ten explanatory variables, i.e., size (total assets), post profitability (OPR, NPR), age, advertising intensity, retention ratio, liquidity, efficiency ratios (inventory turnover ratio, debtor turnover ratio, asset turnover ratio), long term finance, market shore, and research and development intensity were chosen for empirical investigation. Multiple regression analysis was used to develop a model to identify the determinants of profitability of firms in this industry. The results revealed that age, efficiency ratio, post profitability, and research and development intensity ore statistically significant in determining the profitability of firms in drugs and pharmaceutical industry.

Fuel ethanol production from Indian agriculture: opportunities and constraints.

Fuel ethanol use is being encouraged in many countries, including India, to reduce dependence on imported fossil fuels and to reduce local pollution and greenhouse gas emissions, as well as to provide support to stagnating sugarcane-based industries. Indian public policy is to use a blend of 10% ethanol with petrol within the next few years. This translates into a large requirement for fuel ethanol. This paper examines the potential suitability of various carbohydrate-based agri-resources for ethanol production in India, and the resources required for this in different agroclimatic regions. The results show that sugarcane has the highest ethanol potential, followed by cassava, potato and cereals. On the basis of growing time (days) in the field, however, the large differences among crops disappear and their ranking at state and district level also changes. It was calculated that the biomass as well as land requirement for fuel ethanol for 2010–11 in India would be small, taking into account the total food grain production and land used for agriculture. Utilization of only 3–7 million tons of damaged food grains or surplus food stocks could meet the requirement for fuel ethanol up to 2010. This may, however, involve trade-offs with food security. Agricultural residues, especially rice straw, currently burnt in north-western India, and causing air pollution and global warming, could be a useful and cheap resource, if the technology for cellulose conversion is made available and is cost-effective. A proper auditing of costs involved in producing biomass for gasohol, their implications for energy security and the environment, and trade-offs with food security is required for policy consideration.

Testing Gibrat's Law: An Empirical Evidence from Indian Industry

According to Gibrats law of Proportionate Effect, the growth rate of a given firm is independent of its size at the beginning of the examined period. The objective of this paper is to empirically analyze the pattern of corporate growth for an emerging economy, namely India. Balanced time series data of approximately 300 firms over the years 1991–92 to 2005–06 has been used to explore the size and growth relationship. The size of a firm has been measured in terms of net sales, total assets, and market capitalization. The data for various attributes has been taken from ‘PROWESS’ a database of CMIE. SPSS 10.05 has been used to perform the statistical applications.

Methodology for national wheat yield forecast using wheat growth model, WTGROWS, and remote sensing inputs

Wheat is an important food crop of the country. Its productivity lies in a very wide range due to diverse bio-physical and socio-economic conditions in the growing regions. Crop cutting and sample surveys are time consuming as well tedious, and procedure of forecast is delayed. CAPE methodology, which uses remote sensing, ground truth and prevailing weather, has been very successful, but empirical in nature. In a joint IARI-SAC Research Programme, possibility of linking the dynamic wheat growth model with the remote sensing input and other relational database layers was tried. Use of WTGROWS, a wheat growth model developed at IARI, with the remote sensing and relational databases is dynamic and can be updated whenever weather, acreage and fertilizer and other inputs are received. National wheat yield forecast was done for three seasons on meteorological sub-division scale by using WTGROWS, relational database layers and satellite image. WTGROWS was run for historic weather dataset (last 25 years), with the relational database inputs through their associated growth rates and compared with the productivity trends of the met-subdivision. Calibration factor, for each met-subdivision, were obtained to capture the other biotic and abiotic stresses and subsequently used to bring down the yields at each sub-division to realistic scale. The satellite image was used to compute the acreage with wheat in each sub-division. Meteorological data for each-subdivision was obtained from IMD (weekly basis). WTGROWS was run with actual weather data obtained upto a given time, and weather normals use for subsequent period, and the forecast was prepared. This was updated on weekly basis, and the methodology could forecast the wheat yield well in advance with a great accuracy. This procedure shows the pathway for Crop Growth Monitoring System (CGMS) for the country, to be used for land use planning and agri-production estimates, which although looks difficult for diverse agro-ecologies and wide range of bio-physical and socio-economic characters contributing to differential productivity trends.