Datasets for the development of hemp (Cannabis sativa L.) as a crop for the future in tropical environments (Malaysia).

An evidence base was developed to facilitate adoption of hemp (Cannabis sativa L.) in tropical environments (Wimalasiri et al. (2021)). Agro-ecological requirements data of hemp were acquired from international databases and was contrasted against local climate and soil conditions using an augmented species ecological niche modeling. The outputs were then used to map the suitability for all locations for 12 possible calendar-year seasons within peninsular Malaysia. The most probable seasonal map was then used to generate a land suitability map for agricultural areas across 5 standard land suitability categories. Having developed the general suitability maps of hemp in Malaysia, detailed crop growth data were collected from literature and was then used to simulate an ideotype crop model (for both seed and fiber) for selected locations across Malaysia, where detailed daily climate data and soil information were available. Following the development of a downscaled future climate dataset, a simulated dataset of yield for the future conditions were also developed. Next, the simulated seed and fiber yield data were used to create yield maps for hemp across peninsular Malaysia. An economic value and cost-benefit analyses were also carried out using data that were collected from literature and local sources to simulate the true cost and benefit of growing hemp both for now and future conditions. This data provides the first ever evidence base for an underutilized crop in Southeast Asia. All data that was generated using the proposed published framework for the adoption of hemp in the future are stored in their original format in an online repository and is described in this article. The data can be used to map the suitability at finer scales, analyze and re-calibrate a yield model using any climate scenario and evaluate the economics of production using the standard methodology described in the above-mentioned publication.

Correction: Multi-criteria suitability analysis for neglected and underutilised crop species in South Africa.

[This corrects the article DOI: 10.1371/journal.pone.0244734.].

Investigation of the optimum planting dates for maize varieties using a hybrid approach: A case of Hwedza, Zimbabwe.

Water scarcity and unreliable weather conditions frequently cause smallholder farmers in Zimbabwe to plant maize (Zea mays L.) varieties outside the optimum planting timeframe. This challenge exacts the necessity to develop sowing management options for decision support. The study's objective was to use a hybrid approach to determine the best planting windows and maize varieties. The combination will guide farmers on planting dates, dry spell probability during critical stages of the crop growth cycle and rainfall cessation. To capture farmer's perception on agroclimatic information, a systematic random sampling of 438 smallholders was carried out. An analysis of climatic data during 1949-2012 was conducted using INSTAT to identify the best planting criterion. The best combination of planting criterion and maize varieties analysis was then achieved by optimizing planting dates and maize varieties in the DSSAT environment. It was found that 56.2% of farmers grew short-season varieties, 40.2% medium-season varieties and 3.6% long-season varieties. It was also established that the number of rain days and maize yield had a strong positive relationship (p = 0.0049). No significant association was found amongst maize yield (p > 0.05), and planting date criteria, Depth (40mm in 4 days), the AREX criterion- Agricultural Research Extension (25 mm rainfall in 7 days) and the MET Criterion-Department of Meteorological Services (40 mm in 15 days). Highest yields were simulated under the combination of medium-season maize variety and the AREX and MET criteria. The range of simulated yields from 0.0 t/ha to 2.8 t/ha formed the basis for the development of an operational decision support tool (cropping calendar) with (RMSE) (0.20). The methodology can be used to select the best suitable maize varieties and a range of planting time.

Multi-criteria suitability analysis for neglected and underutilised crop species in South Africa.

Several neglected and underutilised species (NUS) provide solutions to climate change and creating a Zero Hunger world, the Sustainable Development Goal 2. Several NUS are drought and heat stress-tolerant, making them ideal for improving marginalised cropping systems in drought-prone areas. However, owing to their status as NUS, current crop suitability maps do not include them as part of the crop choices. This study aimed to develop land suitability maps for selected NUS [sorghum, (Sorghum bicolor), cowpea (Vigna unguiculata), amaranth and taro (Colocasia esculenta)] using Analytic Hierarchy Process (AHP) in ArcGIS. Multidisciplinary factors from climatic, soil and landscape, socio-economic and technical indicators overlaid using Weighted Overlay Analysis. Validation was done through field visits, and area under the curve (AUC) was used to measure AHP model performance. The results indicated that sorghum was highly suitable (S1) = 2%, moderately suitable (S2) = 61%, marginally suitable (S3) = 33%, and unsuitable (N1) = 4%, cowpea S1 = 3%, S2 = 56%, S3 = 39%, N1 = 2%, amaranth S1 = 8%, S2 = 81%, S3 = 11%, and taro S1 = 0.4%, S2 = 28%, S3 = 64%, N1 = 7%, of calculated arable land of SA (12 655 859 ha). Overall, the validation showed that the mapping exercises exhibited a high degree of accuracies (i.e. sorghum AUC = 0.87, cowpea AUC = 0.88, amaranth AUC = 0.95 and taro AUC = 0.82). Rainfall was the most critical variable and criteria with the highest impact on land suitability of the NUS. Results of this study suggest that South Africa has a huge potential for NUS production. The maps developed can contribute to evidence-based and site-specific recommendations for NUS and their mainstreaming. Also, the maps can be used to design appropriate production guidelines and to support existing policy frameworks which advocate for sustainable intensification of marginalised cropping systems through increased crop diversity and the use of stress-tolerant food crops.

Evaluation of Land Suitability Methods with Reference to Neglected and Underutilised Crop Species: A Scoping Review.

In agriculture, land use and land classification address questions such as "where", "why" and "when" a particular crop is grown within a particular agroecology. To date, there are several land suitability analysis (LSA) methods, but there is no consensus on the best method for crop suitability analysis. We conducted a scoping review to evaluate methodological strategies for LSA. Secondary to this, we assessed which of these would be suitable for neglected and underutilised crop species (NUS). The review classified LSA methods reported in articles as traditional (26.6%) and modern (63.4%). Modern approaches, including multi-criteria decision-making (MCDM) methods such as analytical hierarchy process (AHP) (14.9%) and fuzzy methods (12.9%); crop simulation models (9.9%) and machine learning related methods (25.7%) are gaining popularity over traditional methods. The MCDM methods, namely AHP and fuzzy, are commonly applied to LSA while crop models and machine learning related methods are gaining popularity. A total of 67 parameters from climatic, hydrology, soil, socio-economic and landscape properties are essential in LSA. Unavailability and the inclusion of categorical datasets from social sources is a challenge. Using big data and Internet of Things (IoT) improves the accuracy and reliability of LSA methods. The review expects to provide researchers and decision-makers with the most robust methods and standard parameters required in developing LSA for NUS. Qualitative and quantitative approaches must be integrated into unique hybrid land evaluation systems to improve LSA.

Optimizing Traditional Cropping Systems Under Climate Change: A Case of Maize Landraces and Bambara Groundnut.

Traditional crop species are reported to be drought-tolerant and nutrient-dense with potential to contribute to sustainable food and nutrition security within marginal production systems under climate change. We hypothesized that intercropping maize landraces (Zea mays L.) with bambara groundnut (Vigna subterranea (L.) Verdc.), together with optimum management strategies, can improve productivity and water use efficiency (WUE) under climate change. Using an ex-ante approach, we assessed climate change impacts and agronomic management options, such as plant ratios, and plant sequences, on yield and WUE of intercropped maize landrace and bambara groundnut. The Agricultural Production Systems sIMulator (APSIM) model was applied over four time periods; namely past (1961-1991), present (1995-2025), mid-century (2030-2060) and late-century (2065-2095), obtained from six GCMs. Across timescales, there were no significant differences with mean annual rainfall, but late century projections of mean annual temperature and reference crop evaporation (ET0) showed average increases of 3.5°C and 155mm, respectively. By late century and relative to the present, the projected changes in yield and WUE were -10 and -15% and 5 and 7% for intercropped bambara groundnut and maize landrace, respectively. Regardless of timescale, increasing plant population improved yield and WUE of intercropped bambara groundnut. Asynchronous planting increased yield and WUE for both maize landrace (5 and 14%) and bambara groundnut (35 and 47%, respectively). Most significant improvements were observed when either crop was planted 2-3 months apart. To reduce yield gaps in intercrop systems, low-cost management options like changing plant populations and sequential cropping can increase yield and WUE under projected climate change. To further increase sustainability, there is a need to expand the research to consider other management strategies such as use of other traditional crop species, fertilization, rainwater harvesting and soil conservation techniques.

Developing a Roadmap for Improving Neglected and Underutilized Crops: A Case Study of South Africa.

Reports of neglected and underutilized crops' (NUS) potential remain mostly anecdotal with limited and often incoherent research available to support them. This has been attributed to lack of clear research goals, limited funding directed at NUS and journal apathy toward publishing work on NUS. The latter points also explain the lack of interest from emerging and established researchers. Additionally, the NUS community's inability to articulate a roadmap for NUS' promotion may have unintentionally contributed to this. The current study is a sequel to an initial study that assessed the status of NUS in South Africa. The objective of this follow-up study was then to (i) identify priority NUS, and (ii) articulate a strategy and actionable recommendations for promoting NUS in South Africa. The study identified 13 priority NUS, categorized into cereals, legumes, root, and tuber crops and leafy vegetables based on drought and heat stress tolerance and nutritional value. It is recommended that the available limited resources should be targeted on improving these priority NUS as they offer the best prospects for success. Focus should be on developing value chains for the priority NUS. This should be underpinned by science to provide evidence-based outcomes. This would assist to attract more funding for NUS research, development and innovation in South Africa. It is envisaged that through this roadmap, NUS could be transformed from the peripheries into mainstream agriculture. This study provides a template for developing a roadmap for promoting NUS that could be transposed and replicated among the 14 other southern African states.