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Stones on the surface of the soil enhance infiltration and protect the soil against erosion. They are often removed in modern mechanized agriculture, with unfortunate side-effects. We evaluated experimentally the influence of surface stones on infiltration, runoff and erosion under field conditions using a portable rainfall simulator on bare natural soil in semi-arid tropical India, because modernization and mechanization often lead to removal of these stones in this region. Four fields with varied cover of stones from 3 to 65% were exposed to three rainfall intensities (48.5, 89.2 and 136.8 mm hour À1 ). Surface stones retarded surface runoff, increased final infiltration rates, and diminished sediment concentration and soil loss. The final infiltration ranged from 26 to 83% of rainfall when the rainfall intensity was 136.8 mm hour À1 . The reduction in runoff and soil erosion and increase in infiltration were more pronounced where stones rested on the soil surface than where they were buried in the surface layer. The sediment yield increased from 2 g l À1 for 64.7% stone cover with rainfall of 48.5 mm hour À1 to 70 g l À1 for 3.5% stone cover with rain falling at 136.8 mm hour À1 . The soil loss rate was less than 2 t ha À1 hour À1 for the field with stone cover of 64.7% even when the rainfall intensity was increased to 136.8 mm hour À1 . The effects of stones on soil loss under the varied rainfall intensities were expressed mathematically. The particles in the sediment that ran off were mostly of silt size.

The 3 9 2 m spacing currently used for eucalyptus plantations in the state of Andhra Pradesh, southern India does not permit intercropping from the second year. This discourages small landholders who need regular income from taking up eucalyptus plantations and benefiting from the expanding market for pulpwood. Therefore, on-farm experiments were conducted near Bhadrachalam, Khammam district (Andhra Pradesh) for over 4 years from August 2001 to November 2005 to examine whether wide-row planting and grouping of certain tree rows will facilitate extended intercropping without sacrificing wood yield. Eucalyptus planted in five-spatial arrangements in agroforestry [3 9 2 m (farmers' practice), 6 9 1 m, 7 9 1.5 m paired rows (7 9 1.5 PR), 11 9 1 m paired rows (11 9 1 PR) and 10 9 1.5 m triple rows (10 9 1.5 TR)] was compared with sole tree stands at a constant density of 1,666 trees ha -1 . Cowpea (Vigna unguiculata) was intercropped during the post-rainy seasons from 2001 to 2004, and fodder grasses (Panicum maximum and Brachiaria ruziziensis) were intercropped during both the seasons of 2005. At 51 months after planting, different spatial arrangements did not significantly affect height and diameter at breast height (dbh). Total dry biomass of eucalyptus in different spatial arrangements ranged between 59.5 and 52.9 Mg ha -1 , the highest being with 6 9 1 m and the lowest with 10 9 1.5 TR, but treatment differences were not significant. The widely spaced paired row (11 9 1 PR) and triple row (10 9 1.5 TR) arrangements produced 62-73% of sole cowpea yield in 2003, 59-66% of sole cowpea yield in 2004, and 79-94% of sole fodder in 2005. In contrast, the 3 9 2 m spacing allowed only 17-45% of sole crop yields in these years. The better performance of intercrops in widely spaced eucalyptus was likely because of limited competition from trees for light and water. Intercropping of eucalyptus in these wider rows gave 14% greater net returns compared with intercropping M. R. Rao-Former staff of ICRAF (World Agroforestry Centre), Nairobi, Kenya. in eucalyptus spaced at 3 9 2 m, 19% greater returns compared with that from sole tree woodlot and 263% greater returns compared with that from sole crops. Therefore, in regions where annual rainfall is around 1,000 mm and soils are fairly good, eucalyptus at a density of 1,666 plants per ha can be planted in uniformly spaced wide-rows (6 m) or paired rows at an inter-pair spacing of 7-11 m for improving intercrop performance without sacrificing wood production.

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Considering the importance of organic farming and growing demand for organically produced foods, field studies were conducted for 5 years (2004-05 to 2009-10) on a black clayey vertisol soil at the Directorate of Rice Research, Hyderabad, to study the influence of organic and conventional farming systems on productivity, grain quality, soil health and economic returns of super fine rice varieties. Two main plot treatments, with and without plant protection, and four sub plot treatments viz., Control; 100% inorganics; 100% organics; and 50% inorganics+50% organics (integrated nutrient management, INM) were imposed. During wet season, grain yields under 100% inorganics and INM were near stable (4.7-5.5 t/ha) and superior to organics by 15-20% during the first two years, which improved with organics (4.8-5.2 t/ha) in the later years to comparable levels with inorganics, while it had taken five years during dry season. Moderate improvement in nutritional quality was recorded with organics, especially in brown rice. There was a significant improvement in soil physical, fertility and biological properties with organics, which resulted in further improvement in soil quality indices. The sustainability index of the soil was maximum with organics (1.63) compared to inorganics (1.33), after five years of study. The soil organic carbon (SOC) stocks were higher with organics by 44 and 35%, compared to conventional system during wet and dry seasons, respectively, after five years of study. The carbon sequestration rate was also positive with organics (0.97 and 0.57 t/ha/yr during wet and dry seasons, respectively), compared to conventional system that recorded negative SOC sequestration rate (-0.21 and-0.33 t/ha/yr during wet and dry seasons, respectively). Benefit cost ratio was less with organics in the initial years and improved later over inorganics by fifth year.

Assessing vulnerability to climate change and variability is an important first step in evolving appropriate adaptation strategies to changing climate. Such an analysis also helps in targeting adaptation investments, specific to more vulnerable regions. Adopting the definition of vulnerability given by IPCC, vulnerability was assessed for 572 rural districts of India. Thirty eight indicators reflecting sensitivity, adaptive capacity and exposure were chosen to construct the composite vulnerability index. Climate projections of the PRECIS model for A1B scenario for the period 2021-2050 were considered to capture the future climate. The data on these indicators were normalized based on the nature of relationship. They were then combined into three indices for sensitivity, exposure and adaptive capacity, which were then averaged with weights given by experts, to obtain the relative vulnerability index. Based on the index, all the districts were divided into five categories with equal number of districts. One more district was added to 'very high' and 'high' categories. The analysis showed that districts with higher levels of vulnerability are located in the western and peninsular India. It is also observed that the highly fertile Indo-Gangetic Plains are relatively more sensitive, but less vulnerable because of higher adaptive capacity and lower exposure.

The present study features the estimation of number of generations of tobacco caterpillar, Spodoptera litura. Fab. on peanut crop at six locations in India using MarkSim, which provides General Circulation Model (GCM) of future data on daily maximum (T.max), minimum (T.min) air temperatures from six models viz., BCCR-BCM2.0, CNRM-CM3, CSIRO-Mk3.5, ECHams5, INCM-CM3.0 and MIROC3.2 along with an ensemble of the six from three emission scenarios (A2, A1B and B1). This data was used to predict the future pest scenarios following the growing degree days approach in four different climate periods viz., Baseline-1975, Near future (NF) -2020, Distant future (DF)-2050 and Very Distant future (VDF)—2080. It is predicted that more generations would occur during the three future climate periods with significant variation among scenarios and models. Among the seven models, 1–2 additional generations were predicted during DF and VDF due to higher future temperatures in CNRM-CM3, ECHams5 & CSIRO-Mk3.5 models. The temperature projections of these models indicated that the generation time would decrease by 18–22% over baseline. Analysis of variance (ANOVA) was used to partition the variation in the predicted number of generations and generation time of S. litura on peanut during crop season. Geographical location explained 34% of the total variation in number of generations, followed by time period (26%), model (1.74%) and scenario (0.74%). The remaining 14% of the variation was explained by interactions. Increased number of generations and reduction of generation time across the six peanut growing locations of India suggest that the incidence of S. litura may increase due to projected increase in temperatures in future climate change periods.