Alfisol soils of rainfed semi-arid tropics (SAT) are degrading due to several physical, chemical, and biological constraints. Appropriate soil-nutrient management practices may help to check further soil degradation. A long-term experiment comprising tillage and conjunctive nutrient use treatments under a sorghum (Sorghum bicolor (L.) Moench)-mung bean (Vigna radiata (L.) Wilkzec) system was conducted during 1998-05 on SAT Alfisols (Typic Haplustalf) at the Central Research Institute for Dryland Agriculture, Hyderabad. The study evaluated soil and nutrient management treatments for their long-term influence on soil quality using key indicators and soil quality indices (SQI). Of the 21 soil quality parameters considered for study, easily oxidisable N (KMnO 4 oxidisable-N), DTPA extractable Zn and Cu, microbial biomass carbon (MBC), mean weight diameter (MWD) of soil aggregates, and hydraulic conductivity (HC) played a major role in influencing the soil quality and were designated as the key indicators of 'soil quality' for this system. The SQI obtained by the integration of key indicators varied from 0.66 (unamended control) to 0.83 (4 Mg compost þ 20 kg N as urea) under conventional tillage (CT), and from 0.66 (control) to 0.89 (4 Mg compost þ 2 Mg gliricidia loppings) under reduced tillage (RT). Tillage did not influence the SQI, whereas the conjunctive nutrient-use treatments had a significant effect. On an average, under both CT and RT, the sole organic treatment improved the soil quality by 31.8% over the control. The conjunctive nutrient-use treatments improved soil quality by 24.2-27.2%, and the sole inorganic treatment by 18.2% over the control. Statistically, the treatments improved soil quality in the following order: 4 Mg compost þ 2 Mg gliricidia loppings > 2 Mg Gliricidia loppings þ 20 kg N as urea = 4 Mg compost þ 20 kg N as urea > 40 kg N as urea. The percentage contribution of the key indicators towards the SQI was: MBC (28.5%), available N (28.6%), DTPA-Zn (25.3%), DTPA-Cu (8.6%), HC (6.1%), and MWD (2.9%). The functions predicting the changes in yield and sustainability yield index with a given change in SQI were also determined.Additional keywords: semi-arid tropics, soil quality indicators, sorghum-mung bean, sustainability yield index.

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.

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Subsistence agriculture under rainfed conditions and declining or stagnant yields on irrigated farmland has raised concerns about resource management and long-term sustainability in the subtropical, semiarid region of India. Soil quality assessment has been recognized as an important step toward understanding the effects of land management practices within an agricultural watershed. This study addressed the spatial variability ofsoil properties and their quality at the watershed level using geostatistical methods. Soil samples from the 0-to 20-cm depth were collected from i 18 locations on a 100-by 100-m grid across an 88-ha watershed at Sakaliseripalli village in the Nalgonda District in Andhra Pradesh State, India. Geostatistical analysis showed that most ofthe soil parameters were moderately spatially dependent. An assessment framework, including a minimum data set, linear scoring technique, and additive indices, was used to evaluate the soil quality index (SQI). Principal component analysis identified cation exchange capacity, exchangeable Na percentage, DTPA-extractable Zn, available P, available water, and dehydrogenase activity as the most important indicators for evaluating soil quality. A kriged map of SQI was prepared for the watershed. The SQI was higher in irrigated systems (3.01) than under rainfed conditions (2.53), and it was 2.61 and 2.53 in fallow and permanent fallow fields, respectively. In this study, potential soil loss calculated using the Universal Soil Loss Equation and crop yield were identified as the quantifiable management goals; the results indicated that good soils having higher soil quality indices were also productive and less erosion prone.

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