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Farmers' decisions on crop choice, management practices, and livelihood strategies are essential to agricultural sustainability. This data article describes three datasets on crop production in Quzhou, a county in the central part of North China Plain. The three datasets cover different scales. The village dataset assembles basic data on all 342 villages of Quzhou county, including information on population, land area, crop grown, labour, irrigation and markets. Data was sourced from the yearbook data of 2017 and a village cadres survey in 2018. The village dataset was used to create a village typology from which 35 villages belonging to seven village types (five for each type) were selected for stratified random sampling to collect information on farm characteristics and cropping practices. We surveyed these 35 villages, interviewing fifteen farmer households per village (525 in total) in 2020. The interviewees represented two farm management models: smallholder farms and business farms. The resulting household dataset provides farm-level data, including demographic data of farming decision-makers and the number of household members, land use and machinery resources, crop production management practices, and government subsidies. The crop-level dataset was derived from the household survey and included input-output inventories for each crop grown during one year on each field greater than 1/30th ha (1/2 mu) on the 525 surveyed farms within a year. This dataset comprises information on cropping practices in 1352 fields. The three datasets provide a basis for analyses on cropping practices and sustainability attributes of farms and crops in a typical agricultural county on the North China Plain.

Rainfall variability poses a great challenge to rainfed cropping in sub-Saharan Africa. We evaluated, over three cropping seasons, conservation agriculture (reduced tillage and mulching) and farmer prioritized conventional tillage- and mulching-based options on seasonal soil water retention, and subsequent productivity of maize (Zea mays L.) and cowpea (Vigna unguiculata Walp L.) in mono- and inter-crops in Eastern Zimbabwe. The experiments were established on sand and clay soils. The first cropping season (2014/15) received evenly distributed rainfall (hereinafter referred to as ‘wetter’), while the two succeeding seasons (2015/16 and 2016/17) had high incidences of intra-seasonal dry spells (hereinafter referred to as ‘drier’). Overall, conventional tillage had 10–31% and 27–40% more moisture than conservation agriculture treatments on sand and clay soils, respectively. Soil moisture was most retained in intercrop under mulch-based conventional tillage. Maize grain yield during the ‘wetter’ season on sand soil was highest and least (P > 0.05) in intercrop under conservation agriculture (2.3 Mg ha−1) and mulch-based conventional tillage (1 Mg ha−1), respectively. On clay soil, intercrop under mulch-based conventional tillage (2.4 Mg ha−1) yielded the best. During the ‘drier’ seasons, intercrop under mulch-based conventional tillage achieved the best maize grain yield on both sand (1.5 Mg ha−1) and clay (1.4 Mg ha−1) soils. Mulching increased maize grain yield by 55–90% during the ‘drier’ seasons, but reduced water use efficiency (WUE) by approximately 15% during the ‘wetter’ season. Over the three seasons, cowpea grain yield did not exceed 1 Mg ha−1 in both mono- and inter-crops. The study revealed contrasting short-term effects of soil water management options on soil moisture retention and intercropping productivity as dictated by seasonal rainfall variability and soil type. These findings point to the need for tillage and mulching typologies across soil types to minimize negative effects of rainfall variability on crop productivity.

Understanding how to improve the accessibility and timely dissemination of weather and market information can help farmers adapt their management to climate change impacts. Our objective is to use Social Network Analysis (SNA) as a tool to identify potential opportunities for improving weather and market advisory dissemination to rural communities and to explore the relationship between farmers’ access to information and yield and selling price. As a case study, we applied SNA to 313 farmers in Bangladesh to analyze weather and market information networks and farmers' friendship networks as a potential proxy for information exchange. Farmer access to information, dominant sources of information and potential speed of information transfer were key criteria for our analysis. Our results indicate that weather and market information networks in coastal Bangladesh depended on certain key sources of information, while the friendship network was decentralized and interconnected, with few isolated farmers. We showed that farmers networks are significantly correlated with several socio-agro-economic variables; however, there was little indication of a relationship between information access and yield and selling price. We conclude that a mixed approach of targeting central actors and broadcasting information to farmers may be a suitable strategy to reach a maximum number of farmers as well as the most isolated farmers. Our study highlights that SNA can be a promising tool to reveal hidden structures of information flows in farmer communities and provide valuable insights for developing information dissemination strategies that reach even the most remote and underserved farmers.

Abstract A poor understanding of the interactions between biophysical and social elements within rural mountainous landscapes can lead to suboptimal management and recommendations. The objective of this study was to contribute to more contextualized natural resource management in a rural landscape in the Ecuadorian Andes by (1) identifying biophysical patterns in soil properties, biodiversity, and C stocks that emerge from natural landscape pedogenic processes, resulting from elevation‐induced climate gradients, erosion and soil textural patterns, and (2) assessing farm management and land‐use effects on and their interactions with these biophysical patterns. Our findings revealed that the climate and soil texture gradients within the landscape led to an exponential increase in SOC with elevation moderated by slope gradient, indicating significant erosion processes. Farmers adapted their farm management according to the observed environmental patterns creating three distinct management zones. Differentiated agricultural management in these zones and asymmetrical distribution of land‐uses in turn were observed to significantly influence soil and agroecosystem properties. For example, available P was found to be significantly higher in the upper and middle agricultural management zones (24.0 and 28.7 mg/kg, respectively), where agricultural inputs were higher compared to the lower agricultural management zone (8.9 mg/kg, P

This eco-type map presents land units with distinct vegetation and exposure to floods (or droughts) in three villages in the Barotseland, Zambia. The knowledge and eco-types descriptions were collected from participatory mapping and focus group discussions with 77 participants from Mapungu, Lealui, and Nalitoya. We used two Landsat 8 Enhanced Thematic Mapper (TM) images taken in March 24th and July 14th, 2014 (path 175, row 71) to calculate water level and vegetation type which are the two main criteria used by Lozi People for differentiating eco-types. We calculated water levels by using the Water Index (WI) and vegetation type by using the Normalized Difference Vegetation Index (NDVI). We also calculated the Normalized Burn Ratio (NBR) index. We excluded burned areas in 2014 and built areas to reduce classification error. Control points include field data from 99 farmers’ fields, 91 plots of 100 m2 and 65 waypoints randomly selected in a 6 km radius around each village. We also used Google Earth Pro to create control points in areas flooded year-round (e.g., deep waters and large canals), patches of forest and built areas. The eco-type map has a classification accuracy of 81% and a pixel resolution of 30 m. The eco-type map provides a useful resource for agriculture and conservation planning at the landscape level in the Barotse Floodplain. Keywords: Thematic map, Landsat-8 satellite data, Barotseland, Vegetation types, Geographical distribution, GIS

The applicability of statistical typologies that capture farming systems diversity in innovation and development projects would increase if their adaptability would be enhanced, so that newly encountered farms can be classified and used to update the typology. In this paper we propose Naïve Bayesian (NB) classification as a method to allocate farms to types by using only a few variables, thus allowing the addition of new entries to a typology. We show for two example datasets that the performance of NB classification is already acceptable when 50% of the original survey dataset to construct the typology is used for training the NB classifier. For our datasets, the performance of Naïve Bayesian classification was improved when probabilities for observations to belong to multiple types were used, requiring a sample size of 30% of the survey dataset. Based on the results in this paper, we argue that NB classification is a powerful and promising statistical approach to increase the adaptability and usability of farm typologies.