Climate security refers to the risks posed by climate change on nations, societies, and individuals, including the possibility of conflicts. As an emerging field of research and public debate, where conceptual definitions are not yet fully agreed upon, gaining insights into global discussions on climate security enables systematizing its various interpretations and framings, mapping thematic priorities, and understanding information gaps that need to be filled. Considering Twitter as an important digital forum for information exchanges and dialogue, the dataset was created through the development of a query strategy based on a snowball scraping technique, which collected tweets containing hashtags related to climate security between January 2014 to May 2023. The dataset comprises 636,379 tweets. Content analysis was performed using text mining and network analysis techniques to generate additional data on sentiment, countries mentioned in the body of tweets, and hashtag co-occurrences. With almost 10 years of data, the utility of this dataset lies in the ability to assess the discursive evolution of a particular topic since its inception.

Silvopastoral systems are an important strategy for sustainable livestock production. However, to expand their implementation, it is crucial to identify and develop forage materials that maintain good production and quality while being tolerant to shade conditions as well as other biotic and abiotic stresses. A field trial was conducted to evaluate the morpho-anatomical and agronomic responses of two Urochloa hybrids (Camello and Talisman) under two light conditions: shade (28 % light intensity) and full exposure. The trial followed a randomised complete block design with split-plot arrangement, where each treatment corresponded to a plot with three replications. Morphological and anatomical parameters were recorded in three technical replicates of each replication. Histological leaf sections were analysed for the percentage of adaxial epidermis, abaxial epidermis, vascular tissue, colourless parenchyma, Kranz sheath, bulliform cells, sclerenchyma, and chlorenchyma. Measurements in leaf included relative chlorophyll concentration, leaf area, leaf length, and leaf width. Evaluations in plant included height and number of tillers. Agronomic parameters such as plant cover and dry biomass were recorded for each plot. Additionally, six leaf imprints were made on the leaf undersides to observe stomatal morphology, and their length was recorded. Furthermore, plants from each treatment were grown in soil-filled tubes within the same plots. Root system photographs were taken, and in three replications per treatment, root length, root diameter, root volume, root surface area, and the depth at which 95 % of roots were concentrated (D95) were determined. These data can be utilised by the scientific community and breeders to conduct analyses and meta-analyses to identify shade tolerance mechanisms and develop genetic materials tolerant to changing climatic conditions while being optimal for use in silvopastoral systems.

This article provides a description of baseline survey data that was collected in Senegal in the regions of Sedhiou and Tambacounda in 2020, respectively, and as part of an agricultural development project aimed at improving the well-being and resilience of farming households. The survey was implemented using a structured questionnaire administered among 1503 households, 70% of whom are women and 30% are young people, in the two regions. This paper contains data that can helps in understanding the socioeconomic well-being and resilience of smallholder farming households, especially among women and youth. This data helps to associate information on: (i) the socioeconomic project area variables, (ii) the extent of use of irrigated and climate change-adapted crops; (iii) the level of soil and water resource management in the study regions; and (iv) the food security and dietary diversity with the well-being and empowerment of women and young smallholder farming households. In addition, the dataset can be used as a baseline or reference point to track the economic empowerment and climate resilience building achieved in the study regions.

This dataset results from controlled experiments that assess the tolerance of Urochloa spp. and Megathyrsus maximus grasses to nymphal and adult spittlebug damage, particularly from Aeneolamia varia, which significantly impacts forage production in Neotropical regions. Data were collected under standardized conditions using high-throughput phenotyping methods, integrating image-capture techniques and analyses to ensure precise and consistent data acquisition. The dataset serves as a foundational resource for developing and validating computer vision models aimed at automated phenotyping, enabling accurate and high-throughput assessment of plant tolerance to spittlebug damage. Researchers can use the dataset to benchmark and compare different methodologies for plant damage assessment, fostering standardization and reproducibility in phenotyping studies.

The use of mineral fertilizer and organic inputs with an improved and local variety of cassava allows (i) to identify nutrient limitations to cassava production, (ii) to investigate the effects of variety and combined application of mineral and organic inputs on cassava growth and yield and (iii) to evaluate the profitability of the improved variety and fertilizer use in cassava production. Data on growth, yield and yield components of an improved and local variety of cassava, economic analysis, soil and weather, collected during two growing cycles of cassava in farmer's fields in the highlands of the Democratic Republic of Congo (DR Congo) are presented. The data complement the recently published paper “Increased cassava growth and yields through improved variety use and fertilizer application in the highlands of South Kivu, Democratic Republic of Congo” (Munyahali et al., 2023) [1]. Data on plant height and diameter were collected throughout the growing period of the crop while the data on the storage root, stem, tradable storage root, non-tradable storage root and harvest index were determined at 12 months after planting (MAP). An economic analysis was performed using a simplified financial analysis whereby additional benefits were calculated relative to the respective control treatments; the total costs included the purchasing price of fertilizers and the additional net benefits represented the revenue from the increased storage root yield due to fertilizer application. The value cost ratio (VCR) was calculated as the additional net benefits over the cost of fertilizer purchase.

Urochloa grasses are widely used forages in the Neotropics and are gaining importance in other regions due to their role in meeting the increasing global demand for sustainable agricultural practices. High-throughput phenotyping (HTP) is important for accelerating Urochloa breeding programs focused on improving forage and seed yield. While RGB imaging has been used for HTP of vegetative traits, the assessment of phenological stages and seed yield using image analysis remains unexplored in this genus. This work presents a dataset of 2,400 high-resolution RGB images of 200 Urochloa hybrid genotypes, captured over seven months and covering both vegetative and reproductive stages. Images were manually labelled as vegetative or reproductive, and a subset of 255 reproductive stage images were annotated to identify 22,340 individual racemes. This dataset enables the development of machine learning and deep learning models for automated phenological stage classification and raceme identification, facilitating HTP and accelerated breeding of Urochloa spp. hybrids with high seed yield potential.

South Asia's Eastern Indo-Gangetic Plains (EIGP) is home to approximately 450 million people. This region is characterized by the highest global concentration of rural poverty and a predominant reliance on agriculture for nutritional sustenance and economic livelihoods. Agriculture in the EIGP is highly cereal-centric, making crop diversification indispensable for its development. This data article is part of the research conducted by an interdisciplinary team of researchers analyzing the status and determinants of crop diversification in South Asia's EIGP. The data presented here were collected from 1,400 farm households across 72 communities in eight locations within the EIGP of India, Nepal, and Bangladesh during the year 2023. The research employed a simple random sampling method for empirical data collection. The primary agricultural decision-makers were given a tailored questionnaire comprising seven modules. These modules sought comprehensive data on livelihood practices, changes in agriculture, aspirations, diet, food security, mechanization, demographics, and asset ownership. The questionnaire was translated from English into Nepali and Bangla to facilitate a thorough understanding of the farmers' livelihoods in the study areas. The survey successfully ended with 1400 properly filled and captured questionnaires, which was quite representative. The cross-sectional data presented here describe location-specific farm-level crop distribution, enabling the analysis of geographic variations in crop diversification. The generation of this dataset addresses a significant gap in the availability of information on the current state of crop diversification in the EIGP, offering a foundational baseline for future research and interventions by regional governments and development partners. We employed the Herfindahl-Hirschman Index (HHI) to calculate crop diversification and a Tobit Regression Model to identify the region-specific determinants of crop diversification. The dataset is hereby made available as it is considered vital for regional policy and practical recommendations.

Mediterranean forests represent critical areas that are increasingly affected by the frequency of droughts and fires, anthropic activities and land use changes. Optical remote sensing data give access to several essential biodiversity variables, such as species traits (related to vegetation biophysical and biochemical composition), which can help to better understand the structure and functioning of these forests. However, their reliability highly depends on the scale of observation and the spectral configuration of the sensor. Thus, the objective of the SENTHYMED/MEDOAK experiment is to provide datasets from leaf to canopy scale in synchronization with remote sensing acquisitions obtained from multi-platform sensors having different spectral characteristics and spatial resolutions. Seven monthly data collections were performed between April and October 2021 (with a complementary one in June 2023) over two forests in the north of Montpellier, France, comprised of two oak endemic species with different phenological dynamics (evergreen: Quercus ilex and deciduous: Quercus pubescens) and a variability of canopy cover fractions (from dense to open canopy). These collections were coincident with satellite multispectral Sentinel-2 data and one with airborne hyperspectral AVIRIS-Next Generation data. In addition, satellite hyperspectral PRISMA and DESIS were also available for some dates. All these airborne and satellite data are provided from free online download websites. Eight datasets are presented in this paper from thirteen studied forest plots: (1) overstory and understory inventory, (2) 687 canopy plant area index from Li-COR plant canopy analyzers, (3) 1475 in situ spectral reflectances (oak canopy, trunk, grass, limestone, etc.) from ASD spectroradiometers, (4) 92 soil moistures and temperatures from IMKO and Campbell probes, (5) 747 leaf-clip optical data from SPAD and DUALEX sensors, (6) 2594 in-lab leaf directional-hemispherical reflectances and transmittances from ASD spectroradiometer coupled with an integrating sphere, (7) 747 in-lab measured leaf water and dry matter content, and additional leaf traits by inversion of the PROSPECT model and (8) UAV-borne LiDAR 3-D point clouds. These datasets can be useful for multi-scale and multi-temporal calibration/validation of high level satellite vegetation products such as species traits, for current and future imaging spectroscopic missions, and by fusing or comparing both multispectral and hyperspectral data. Other targeted applications can be forest 3-D modelling, biodiversity assessment, fire risk prevention and globally vegetation monitoring.