Siza D. Tumbo

Influence of gender on roles, choices of crop types and value chain upgrading strategies in semi-arid and sub-humid Tanzania

Upgrading strategies for a given product value chain might not result in the intended impact on different gender groups, if gender analysis is not undertaken. This study investigated the gender influence on preferred food and cash crops, as well as upgrading strategies in sub-humid Kilosa and semi-arid Chamwino Districts, Tanzania. A mixed methods research design was used to collect information from 595 respondents, while content analysis was used to qualitatively analyze qualitative data obtained from focus group discussions and key informant interviews. Our findings show that farmers from Kilosa and Chamwino had no differences with respect to preferences expressed by men and women for their first priority cash. Gender differences for choices were evident in relation to the second priority, food crops, in the semi-arid area and the third priority, which was also food crops in the sub-humid area, where women and youth differed from men on their views toward maize versus sorghum in the semi-arid region. Here, youth differed from women and men in how they viewed cassava versus rice. For upgrading strategies, which were only conducted with the first priority crops, most of the strategies preferred by men differed from those preferred by women and youth. In both areas, youth and women preferred upgrading strategies related to crop harvesting, transportation and primary processing, whereas men preferred upgrading strategies for farm inputs and crop marketing. Therefore, it is recommended that site-specific gendered analysis on upgrading strategies in agricultural value chains should be completed prior to introducing an intervention.

Trans-SEC’s food security research in Tanzania: principles, research models and assumptions

Food security has become a major worldwide concern and key policy issue (Stephens et al. 2017; Foley et al. 2011). The topic also gained economic importance through global change. New markets, trade liberalisation, volatile price developments and transport issues also shape agricultural production structures (Dithmer and Abdulai 2017). However, while demand and consumption increase in some regions, it is usually the rural poor population that suffers from inadequate food supply, having no market access and facing supply chain risks in remote locations (Stein and Hosaena 2016). Also, the mainly rain-fed agricultural systems are not only very vulnerable to climatic changes (Below et al. 2015), they are dominated by subsistence-oriented smallholder farmers who lack external inputs, modern technology, access to finance, secure land rights, institutional support and adaptive capacity (Shikuku et al. 2017). This holds true for Tanzania (Graef et al. 2015, b; Muthoni et al. 2017). Despite overall positive developments in the past years, a large proportion of the population faces extreme poverty, the majority living in rural households (Muthoni et al. 2017). Tanzania is among those African countries with the highest levels of malnutrition. As a result, a substantial percentage of children suffer from stunted growth (McCullum et al. 2015). The situation is aggravated by above-average population growth. Agriculture is the main source of employment and livelihood for the vast majority of the population (Arndt et al. 2012), But despite its critical role for this East African country, the agricultural sector does not prosper and is characterized by low productivity. Key limiting factors include inadequate infrastructure and storage facilities, lack of incentives for smallholder farmers to produce beyond selfsufficiency or low economies of scale (Asfaw et al. 2012; Uckert et al. 2015). Strategies are needed to close the yield gap sustainably and address the highly complex state of vulnerability encompassing economic and socio-political factors (Candel 2017; Graef et al. 2014, 2015a, b; Sieber et al. 2015a, b; Mutabazi et al. 2015). New research frameworks are therefore needed (Reif et al. 2015). Given this background, there is a strong need to test new research approaches in order to increase efficiencies and effectiveness of agricultural innovations and, in particular, to focus on upand out-scaling. In this paper we:

Unmanned aerial vehicle-based remote sensing in monitoring smallholder, heterogeneous crop fields in Tanzania

ABSTRACT Obtaining information to characterize smallholder farm fields remains elusive and has undermined efforts to determine crop conditions for food security monitoring. We hypothesize that unmanned aerial vehicles (UAV) would provide high-resolution spectral signatures for effectively discerning agronomic and crop conditions, management practices, and yields in smallholder farms for crop yield outlooks. The current study explores potential in using UAV-mounted sensor spectral signatures for monitoring crop conditions in smallholder agriculture. Images were collected using a 4-band multispectral camera mounted on a small fixed wing UAV, flown at 8-day interval over maize–pigeonpea experimental plots at Sokoine University of Agriculture and maize monocrop in farmers’ fields nearby, during 2015/2016 growing season. Four spectral vegetation indices (VIs) namely; normalized difference vegetation index (NDVI), wide dynamic range vegetation index (WDRVI), red edge chlorophyll index (CIred-edge), and the green chlorophyll index (CIgreen), were evaluated under maize monocrop, maize pigeonpea-intercrop, fertilizer and non-fertilizer and two maize varieties conditions. VIs were used also to detect differences in farm management practices of two farmers’ maize fields. The response of the spectral VIs varied depending on phenological stage of the crop and imposed treatments or management practices. In experimental plots, NDVI was able to distinguish fertilized from non-fertilized plots at all times, distinguish between two maize varieties at 52 days after sowing (DAS), and differentiate monocropped maize from maize–pigeonpea intercrop at 60 DAS. CIred-edge could detect effect of maize–pigeonpea intercrop and maize varieties at 44 DAS, whereas CIgreen could detect variety differences at 44 DAS, intercropping effect at all times and fertilizer effects at 60 and 68 DAS. WDRVI could only detect variety differences and maize–pigeonpea intercrop at 44 DAS. Moreover, NDVI was slightly associated with maize yield in non-fertilized plots (coefficient of determination – R2 = 0.58) and CIgreen was associated with leaf area index (LAI) (R2 = 0.62) in fertilized plots and in monocropped plots (R2 = 0.61). CIgreen could also differentiate well managed from poorly managed farmer’s fields. We conclude that UAV-derived spectral signatures can provide detailed information for characterizing agronomic and crop conditions under smallholder agricultural settings and aid food security monitoring efforts.

Trans-SEC’s food security research in Tanzania: from constraints to adoption for out- and upscaling of agricultural innovations

Implementing agricultural innovations is key for coping strategies in the contexts of climate change and food security (Senyolo et al. 2018; Bommarco et al. 2018). The agricultural sector is still the central focus for rural development, especially in remote areas of Sub-Saharan Africa. Links to Small and Medium-size Enterprises (SMEs), among others, are often lacking, due to comparatively high costs for logistics, transport, and communication (Letiche 2010; Meyfroidt 2018; Stephens et al. 2018). Peri-urban and rural areas need specific and tailored livelihood strategies (Fraval et al. 2018). Thus, enabling environments for business models and alternative ecosystem services (Bommarco et al. 2018) are difficult to establish. Low economies of size and scale hinder the establishment of profitable economies (Tomich et al. 2018; Letiche 2010). Nevertheless, manifold implementation models for upgrading agricultural activities do exist and are continuously being tested and adapted in international research projects (Candel 2017). Specific implementation models disseminate innovations despite various structural problems of research and development in Sub-Saharan Africa (Lipton 1988). Among these theoretical models, the main challenge remaining is how agricultural innovations can be disseminated efficiently and effectively through outscaling and upscaling, given varying site conditions and diverse target groups (Senyolo et al. 2018). While pro-poor approaches focus mostly on small-scale farmers, the question of the right setting for agricultural innovations persists. In less favorable areas, typically low-cost innovations are more suitable due to limited capacities (capital), while higher income farmers in favorable production areas might seek higher investments and more revenue through market integration (Tomich et al. 2018). Additionally, at local levels, some farmers are more innovative than others, seeking different agro-ecological transformation strategies (Tittonell 2014). These superior performing farmers are more likely to adopt new techniques, even if they are riskier (Steinke and van Etten 2018). Innovators and catalyzers are key to more efficient and reliable adoption of agricultural innovations (Steinke and van Etten 2018; Below et al. 2015; Uckert et al. 2015). The issue of innovation adoption is an important research topic bridging the gap between Bmaximum yield potential^ and actual yields harvested in farmers’ fields (Foley et al. 2011). Such research should be long term and monitored over time in order to arrive at sustainable improvements. Agricultural innovations should be continually optimized in response to changing conditions (Tomich et al. 2018; Mutabazi et al. 2015, Senyolo et al. 2018, Below et al. 2015). Furthermore, an adequate incentive structure is a necessity for the long-term adoption of successful techniques (Nhantumbo et al. 2016). These applied adoption theories are closely linked and indispensably coupled with outand upscaling methods, which seek efficient and effective horizontal and vertical * Stefan Sieber stefan.sieber@zalf.de

Prediction of Soil Moisture-Holding Capacity with Support Vector Machines in Dry Subhumid Tropics

Soil moisture-holding capacity data are required in modelling agrohydrological functions of dry subhumid environments for sustainable crop yields. However, they are hardly sufficient and costly to measure. Mathematical models called pedotransfer functions (PTFs) that use soil physicochemical properties as inputs to estimate soil moisture-holding capacity are an attractive alternative but limited by specificity to pedoenvironments and regression methods. This study explored the support vector machines method in the development of PTFs (SVR-PTFs) for dry subhumid tropics. Comparison with the multiple linear regression method (MLR-PTFs) was done using a soil dataset containing 296 samples of measured moisture content and soil physicochemical properties. Developed SVR-PTFs have a tendency to underestimate moisture content with the root-mean-square error between 0.037 and 0.042u2009cm3·cm−3 and coefficients of determination (R2) between 56.2% and 67.9%. The SVR-PTFs were marginally better than MLR-PTFs and had better accuracy than published SVR-PTFs. It is held that the adoption of the linear kernel in the calibration process of SVR-PTFs influenced their performance.