The implementation of new agricultural technologies is considered a key component of Malawi’s agricul- tural development strategy. However, successful technology adoption has proven to be difficult, affecting both farmers and small businesses introducing the technology. With a primary focus on farmers interested in adopting the technology, common factors influencing farmers’ adoption habits are determined by con- ducting interviews and observations. Survey results indicate that from a farmers perspective; market ac- cess, information access, education, and affordability are key factors helping facilitate adoption, while lack of these factors hinder adoption. Insight into these factors could provide business knowledge on how to better facilitate technology adoption. For this purpose, literature factors affecting a SME’s success are compared with an active agricultural technology company. These factors include experience, communication, man- agement and technology performance. Using both farmers needs and business developmental factors, this study hopes to provide insights for new companies starting a business in a developing country.

For cotton cultivation, large amounts of water are necessary. One of the countries facing water challenges related to cotton cultivation is India. India experiences a monsoon weather pattern causing large amount of rain from June to October and no rain during the other months. One of the challenges India’s small holder cot- ton farmers face is the lack of water after the monsoon. A solution to this problem could be the construction of (small) reservoir structures and renovation of currently present structures. One of the main questions arising here is ‘how can a hydrological model estimate new reservoir locations for small holder cotton farmers and what is the potential of these new structures and current structures in Maharashtra India with Ghatanji block and Hinganghat block as case study areas?’. Estimating potential reservoir locations as well as determining which current reservoir structures have the potential to be renewed could increase the water availability for small holder cotton farmers in Maharashtra, India.
Four focus areas are chosen, these areas are Yavatmal, Nagpur, Wardha and Amravati. Within these fo- cus areas, Ghatanji block and Hinganghat block are used as case study areas for developing and validating different models used for analysing current and suitable potential locations.Remote data based tools used to locate, validate and analyse the potential of current and potential suitable structures are QSWAT (hydrological model), Google Earth Engine (land cover analysis), Python coding, Zoom.Earth (locating current structures), socio-hydrological model (yield and benefit analysis).
Out of the initial 692 potential locations, 315 locations in Ghatanji block and 349 locations in Hinganghat block are classified as suitable potential reservoir locations. A location was marked suitable if a minimum size (200m3) reservoir on this position has the ability to fill up completely at least once a year for an average monsoon season. Additionally, the structure was positioned in a location with more than 30% crop coverage and less than 25% urban coverage. Out of a total of 2212 current structures, 840 locations in Hinganghat and 970 locations in Ghatanji have a high potential of collecting water if renovated and in use. This high potential is a result of small distance to the nearest stream and suitable land cover on the position of the reservoir. Existing structures were classified as suitable if they adhere the same prerequisites as stated above regarding the coverage and for the additional requirement that a structure should be located less than 200 meters from the nearest stream point.
The results of this investigation show that a hydrological model can estimate new potential reservoir locations and that the use of a hydrological model is beneficial for locating these potential locations when com- pared to current or recently constructed locations. The benefit of using a hydrological model is that it can identify the surface runoff paths of water during and after a precipitation event. Furthermore, can remote data based tools help in identifying the suitability of both potential as well as current structures.