This report represents the baseline study of the Project "Cotton Water", a collaboration between Solidaridad Asia, TU Delft and other participating instututions to improve the livelihood of cotton farmers in the Vidarbha and Marathwada regions of Maharashtra, India. This baseline study was divided in three main steps: a desktop study, where high resolution maps of precipitation, potential evaporation, soil type and landuse were used in conjunction with a smallholder socio-hydrological model to identify 'hotspots' where farmers' capital falls below poverty lines; a field survey, in which farmers were extensively questioned on their financial situation and farming practices as well as their perception of water scarcity and irrigation schemes; and a final synthesis where interventions are analysed with the smallholder socio-hydrological model and a psycho-social analysis of farmer behaviour is delivered alongside a mapping of current water productivity of cotton in the study area. The main results found are that the proposed water harvesting and recharge interventions increase and stabilize yields, and the overall effect on capital are moderate. Other factors that do not impact water availability including fertilizer and labour were found to have notable impacts and should be well understood to accurately improve farmers' situations. Financial aspects including cotton sale prices and loan interest rates had strong impacts on farmers' capital development as well, particularly with high interest rates punishing some farmers. An analysis of good- and poor-performing farmers demonstrated that irrigation in general and micro-irrigation did improve probability of good farmer performance as did increased yields. Older men also showed higher rates of profit, demonstrating that the impact of experience may increase profit margins, even if it necessarily doesn't increase likelihood to adopt interventions. What was found to increase probability of adopting irrigation and irrigation technology was low promotion exposure. It is hypothesized that increased promotion may influence many farmers negatively, fostering an attitude of despair rather than informing them of opportunity. The psycho-social evaluation also found that solutions that are reasonably expensive but not too costly have higher chances of being adopted. Four main recommendations were made to help improve farmer welfare with respect to the scope specified. It was recommended to: limit promotion and to be more selective and positive with the message; focus on localized water storage interventions to increase farmers' access to water; regulate cotton prices through government intervention or contracts with clothing companies to decrease vulnerability to price changes; and improve access to loans from the government and reduce the role of money lenders who often are the ones charging the greatest interest rates.

The water quality of the Brantas river in Indonesia is of concern to several agencies on East Java. These agencies all measure its water quality in their own way in terms of locations, rhythms and parameters. The goal of this thesis is to find out if these agencies measure the same and if not, how these measurements differ. From these measurements, perspectives are constructed for each agency with the use of Principal Component Analysis. The agencies investigated are the Dinas Lingkungan Hidup Jawa Timur (DLH Jatim), Belai Besar Wilayah Sungai Brantas (BBWS) and Perum Jasa Tirta I (PJT). As an addition to the PCA, a neural network model is constructed and trained to recognize the measurement agency of a datapoint from the measurement values. It was found that the all three agencies recognized oxygen as a dominant driver in water quality processes. Secondary processes were mostly driven by rainfall, but the effect of this was seen differently by the agencies. DLH Jatim distinguishes surface waste runoff separately from rainfall, while Perum Jasa Tirta I will see them as inherently connected. BBWS will not recognize the surface waste runoff process as a significant factor in the water quality. These differences found in the representation of core processes in the Brantas outline how different agencies can have a different perspective on water quality. This was further underlined by the conclusions from the neural network analysis. Here it was found that the author could be recognized from the measurement values alone on 88% of agency data.