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.

Iron oxides and aluminous clays selectively control soil carbon storage and stability in the humid tropics.

Clay minerals and pedogenic metal (oxyhydr)oxides are the most reactive soil mineral constituents controlling the long-term persistence of organic carbon (OC) in terrestrial ecosystems. However, their co-occurrence in most soils complicates direct assessment of their individual contribution to OC persistence. Making use of unique mineralogical combinations in soils located in the East Usambara Mountains of Tanzania, we disentangled the contribution of clay-sized aluminous minerals (kaolinite, gibbsite) and pedogenic Fe (oxyhydr)oxides (predominant goethite and hematite) on OC storage and stabilization under natural forests and croplands. Topsoil samples, varying in contents but not types of aluminous clays and pedogenic Fe (oxyhydr)oxides, were identified by selective extractions, X-ray diffraction, and Mössbauer spectroscopy. Associated abundance of particulate and mineral-associated organic matter (OM) was quantified by density fractionation and their changes during land-use conversion were determined as a measure of OC persistence. Additionally, we assessed the resistance of OC to chemical oxidation as well as microbial decomposition in a 50-day laboratory incubation. We found that the ratio of pedogenic Fe to aluminous clay is more consequential for OC storage and stabilization than their individual contents, despite the fact that Fe (oxyhydr)oxides generally exert a stronger impact on OC than aluminous clays. Conjunction of large amounts of Fe (oxyhydr)oxides with low aluminous clay contents caused the strongest accumulation of mineral-associated OC, a low soil respiration, high OC stability against chemical oxidation, and high OC persistence during land-use change. Our study suggests that certain mineralogical combinations in the humid tropics alleviate OM losses during land conversion because of the strong and selective mineral control on OC stabilization, particular if the weight ratio of pedogenic Fe to aluminous clay exceeds the threshold range of 0.44‒0.56.

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.

Changes in catchment conditions lead to enhanced remobilization of arsenic in a water reservoir.

Increasing arsenic concentrations in freshwater ecosystems is of global concern. Processes affecting arsenic fluxes in catchments are known. These processes are in turn controlled by the underlying geology and air pollution history. In contrast to the knowledge on catchment processes less is known about the hydrochemical processes controlling the fixation/remobilization of arsenic within lakes and artificial reservoirs. Consequently, we examined a reservoir system in the Ore Mts. (Germany) regarding its sink and source potentials affecting arsenic fluxes. This area was faced with heavy deposition inputs from coal burning based acid rain until the beginning of the 1990s. Hereafter concentrations of sulfate and nitrate in runoff waters decreased, whereas dissolved organic carbon (DOC) concentrations are still increasing. Along with this, arsenic concentrations in the water discharge from the catchments increase. Our results reveal that the sediments of the investigated reservoir system contain high inventories of arsenic in association with ferric and organic phases. A nitrate deficit dependent arsenic release is suggested. It is indicated that arsenic release from the reservoir sediments may be controlled by water nitrate concentration, which in turn is dependent on the nitrate concentration in the runoff water from the catchment.