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Record W4406633489 · doi:10.1002/fes3.70046

A Blueprint for Building Resilience and Food Security in <scp>MENA</scp> and <scp>SSA</scp> Drylands: Diversifying Agriculture With Neglected and Underutilized Species

2025· article· en· W4406633489 on OpenAlex
Krishna Prasad Devkota, Mina Devkota, Tafadzwanashe Mabhaudhi, Vinay Nangia, Samar Attaher, R.J. Boroto, Jagadish Timsina, Kadambot H. M. Siddique

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFood and Energy Security · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsUnited Nations University Institute for Water, Environment, and Health
FundersConsortium of International Agricultural Research CentersInternational Center for Agricultural Research in the Dry Areas
KeywordsResilience (materials science)Food securityBusinessAgricultureBlueprintNatural resource economicsBiotechnologyBiologyEconomicsEngineeringEcology

Abstract

fetched live from OpenAlex

ABSTRACT Drylands, encompassing 41% of global land and supporting over 2 billion people, face significant challenges, including water scarcity, extreme temperatures, and soil degradation. Dryland spans vast areas of Middle East and North Africa (MENA) and Sub‐Sahara Africa (SSA) regions and poses a threat to food security and resilience. This study examines the potential of neglected and underutilized species (NUS) to improve dryland food and nutrition security, focusing on their agronomic performance, water productivity, economic viability, and nutritional benefits. Using long‐term data from FAOSTAT, USDA Food Data Central, and peer‐reviewed literature, we analyzed trends in the cultivation, yield, and nutritional contributions of 26 NUS across 22 countries in the MENA region comparing them with major staples—rice, wheat, and maize. Between 1961 and 2022, NUS crop areas in MENA fluctuated, decreasing by 7.0% since 2018 to 21.17 Mha. Despite this, NUS demonstrated superior water productivity—up to 30% higher than major cereals. For instance, sorghum and cowpea achieved 2.5 kg/m 3 compared to maize (0.83 kg/m 3 ) and wheat (0.91 kg/m 3 ) and exhibited strong heat tolerance, withstanding temperatures of up to 42°C and 38°C, respectively. Despite a negative trade balance, NUS significantly contributed to dietary calories, surpassing wheat. A field experiment in Merchouch, Morocco, confirmed that NUS offered a higher economic value per unit than wheat, and outperformed conventional crops across key indicators. Integrating NUS into dryland farming systems can enhance food security, sustainability, and resilience to climate change. Advancing NUS requires breeding programs, tailored good agricultural practices, value addition and market linkage, supportive policies, and farmer education. Collaborative efforts among international organizations, governments, and civil society are crucial to mainstreaming NUS in agrifood systems and contributing to the diversity, sustainability, and resilience of dryland farming systems in MENA and SSA regions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.204
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it