Data set of smallholder farm households in banana-coffee-based farming systems containing data on farm households, agricultural production and use of organic farm waste.

The data was collected in the Karagwe and Kyerwa districts of the Kagera region in north-west Tanzania. It encompasses 150 smallholder farming households, which were interviewed on the composition of their household, agricultural production and use of organic farm waste. The data covers the two previous rainy seasons and the associated vegetation periods between September 2016 and August 2017. The knowledge of experts from the following institutions was included in the discussion on the selection criteria: two local non-profit organisations, i.e., WOMEDA and the MAVUNO Project; the International Institute of Tropical Agriculture (IITA); and the National Land Use Planning Commission (NLUPC). Households were selected for inclusion if all of the following applied to them: 1) less than 10 acres of land (4.7 ha) registered in the village offices, 2) no agricultural training, and 3) decline in the fertility of their land since they started farming (self-reported). We selected 150 smallholder households out of a pool of 5,000 households known to WOMEDA in six divisions of the Kyerwa and Karagwe districts. The questionnaire contained 54 questions. The original language of the survey was Kiswahili. All interviews were audio recorded. The answers were digitalised and translated into English. The data set contains the raw data with 130 quantitative and qualitative variables. For quantitative variables, the only analysis that was made was the conversion of units, e.g., land area was converted from acres to hectares, harvest from buckets to kilograms and then to tons, and heads of livestock to Tropical Livestock Units (TLU). Qualitative variables were summarised into categories. All data has been anonymised. The data set includes geographical variables, household information, agricultural information, gender-specific responsibilities, economic data, farm waste management, and water, energy and food availability (Water-Energy-Food (WEF) Nexus). Variables are written in italics. The following geographical variables are part of the data set: district, division, ward, village, hamlet, longitude, latitude, and altitude. Household information includes start of farming, household size, gender and age of household members. Agricultural information includes land size, size of homegarden, crops, livestock and livestock keeping, trees, and access to forest. Gender-specific responsibilities includes producing and exchanging seeds, weed control, terracing, distributing organic material to the fields, care of annual and perennial crops, harvesting of crops, decisions about the harvest and animal products, selling and buying products, working on their own farm and off-farm, cooking, storing food, collecting and caring for drinking water, washing, and toilet cleaning. Economic data includes distance to the market, journey time to market, transport methods, labourers employed by the household, working off-farm, and assets such as type of house. Variables relevant to the WEF Nexus are drinking water source and treatment, meals per day, months without food, cooking fuel, and type of toilet. Variables on farm waste management are the use of crop residues, food and kitchen waste, livestock manure, cooking ash, animal bones, and human urine and faeces. The data can be potentially reused and further developed for the purpose of agricultural production analysis, socio-economic analysis, comparison to other regions, conceptualisation of waste and nutrient management, establishment of land use concepts, and further analysis on food security and healthy diets.

How afforestation affects the water cycle in drylands: A process-based comparative analysis.

The world's largest afforestation programs implemented by China made a great contribution to the global "greening up." These programs have received worldwide attention due to its contribution toward achieving the United Nations Sustainable Development Goals. However, emerging studies have suggested that these campaigns, when not properly implemented, resulted in unintended ecological and water security concerns at the regional scale. While mounting evidence shows that afforestation causes substantial reduction in water yield at the watershed scale, process-based studies on how forest plantations alter the partitioning of rainwater and affect water balance components in natural vegetation are still lacking at the plot scale. This lack of science-based data prevents a comprehensive understanding of forest-related ecosystem services such as soil conservation and water supply under climate change. The present study represents the first "Paired Plot" study of the water balance of afforestation on the Loess Plateau. We investigate the effects of forest structure and environmental factors on the full water cycle in a typical multilayer plantation forest composed of black locust, one of the most popular tree species for plantations worldwide. We measure the ecohydrological components of a black locust versus natural grassland on adjacent sites. The startling finding of this study is that, contrary to the general belief, the understory-instead of the overstory-was the main water consumer in this plantation. Moreover, there is a strict physiological regulation of forest transpiration. In contrast to grassland, annual seepage under the forest was minor in years with an average rainfall. We conclude that global long-term greening efforts in drylands require careful ecohydrologic evaluation so that green and blue water trade-offs are properly addressed. This is especially important for reforestation-based watershed land management, that aims at carbon sequestration in mitigating climate change while maintaining regional water security, to be effective on a large scale.

SPorDyn: A Python code for modeling the evolution of soil pore size distribution after tillage.

Surface soil structure is very responsive to natural and anthropogenic impacts and these changes alter soil hydraulic properties and the soil water budget. In the midst of a dearth of efforts to capture soil structural dynamics, an analytical solution to the Fokker-Planck Equation with physically-based coefficients has shown promising results in predicting the evolution of soil pore space in agricultural soils. In this study, the Python code for the analytical solution is shown along with steps to estimate coefficients leading towards obtaining the analytical solution. •Python code for modeling the evolution of soil pore space based on an existing model is shared.•The code for the estimation of physically-based coefficients of the model and parameter optimization are also shown.•The final output of the model may be used in estimation of soil water retention and hydraulic conductivity functions.