Millets for Food Security in the Context of Climate Change: A Review
Abstract
:1. Introduction
2. Millets Cultivation
3. Methodology Adopted
4. Predicted Climate Changes and Crop Productivity
4.1. Change in Soil Condition
4.2. Depletion of Water Resources
4.3. Effect of Climate Change on Agricultural Productivity
5. Outlook
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Type of Millets | Scientific Name | Common Name | Cultivated Regions | Growing Conditions | References |
---|---|---|---|---|---|
Pearl Millet | Pennisetum typhoides | Bulrush millet | Central, Eastern and Southern Africa; in Western Africa, (Sahel), India and Pakistan along the southern coast of the Arabian Peninsula * | Dry climates, marginal soils, Rainfall 200–500 mm ** | * [11] ** [10] |
Finger millet | Eleusine coracona | Birds food millet or African millet | Parts of India and Africa * | Resist higher temperatures and salinity, temperature 11 to 27 °C, soil pH of 5 to 8.2 and a medium rainfall environment ** | * [13] ** [14] |
Proso Millet | Panicum miliaceum | Common millet, hog millet, broom corn, yellow hog, hershey and white millet | China, India, Russia, in the Middle East including Iran, Iraq, Syria, Turkey Afghanistan and Romania * | Less water, Rainfall less than 600 mm, average temperature 17 °C during daytime ** | * [15,16,17] ** [18] |
Foxtail Millet | Setaria italica | Italian millet, German millet, or hay millet | Europe, China, India, Indonesia, the Korean peninsula and the former U.S.S.R. * | Less water **, short duration *** | * [19] ** [20] *** [22] |
Barnyard Millet | Echinochloa crusgalli var. Frumentacea or E. colona (Sawa) | - | India, China, Japan, Pakistan, Africa and Nepal * | Drought tolerant, rapid maturation rate ** | * [24] ** [26] |
Kodo Millet | Paspalum scorbiculatum | - | Tropical and sub-tropical regions * | High drought resistance, Good yields, period of 80–135 days ** | * [28,29] ** [30] |
Regions or Countries | Model/Methodology | Major Findings | References |
---|---|---|---|
South America | |||
| Revised Universal Soil Loss Equation |
| [50] |
| Soil erosion model (combination of remote sensing, GIS modeling and census data) |
| [51] |
| Water Erosion Prediction Project (WEPP) and climate generator (CLIGEN) Geographic Information system (GIS) |
| [52] |
Universal Transverse Mercator (UTM) and Brazil Geodesic referencing system (SIRGAS2000) |
| [53] | |
Europe | |||
| Monitoring of 86 agricultural fields |
| [54] |
| Revised Universal Soil Loss Equation (RUSLE) |
| [55] |
Rainfall Erosivity Database |
| [56] | |
Cubist Regression Model |
| [57] | |
European Environment Information and Observation Network for soil (EIONET-SOIL) |
| [58] | |
Africa | |||
| Six Regional and Global climate models |
| [59] |
Land Management and Planning Tool |
| [60] | |
| 5 km × 5 km resolution multisource rainfall product (Climate Hazards Group InfraRed Precipitation with Stations) |
| [61] |
| Soil erosion model (combination of remote sensing, GIS modeling and census data) |
| [51] |
| Global Rainfall Erosivity Database (GloREDa) |
| [62] |
Southeastern Asia | |||
| Soil erosion model (combination of remote sensing, GIS modeling and census data) |
| [51] |
Mann-Kendall statistical test and Theil and Sen’s approach |
| [63] | |
| Soil and Water Assessment Tool (SWAT) |
| [64] |
South Asia | |||
| Review of various articles |
| [65] |
Soil erosion model (combination of remote sensing, GIS modeling and census data) |
| [51] | |
| Geographic Information system (GIS) |
| [66] |
Least-square support vector machine (LS-SVM), Statistical downscaling model (SDSM) models and the Universal soil loss equation (USLE) model |
| [67] | |
| Revised Universal Soil Loss Equation (RUSLE) model coupled and transport limited sediment delivery (TLSD) |
| [68] |
China | |||
Analysis of soil erosion rates in fifteen experimental plots |
| [69] | |
In situ bank gully field flume experiment |
| [70] | |
Universal Soil Loss Equation (USLE) |
| [71] |
Regions or Countries | Model | Major Findings | References |
---|---|---|---|
South America | |||
| CLM, Mosaic, Noah, VIC |
| [87] |
Review of various research articles |
| [88] | |
| Collection of rainfall data from various resources |
| [89] |
| Global Circulation Model (GCM) |
| [90] |
| 27 Global climate models |
| [91] |
| Data collection from research projects between 2004 and 2012 |
| [92] |
Europe | |||
| InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) |
| [93] |
| InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) |
| [94] |
| EXIOBASE |
| [95] |
| TOPDM (Topography based probability distribution model) |
| [96] |
| Energy balance models |
| [97] |
| City Blueprint Methodology |
| [98] |
Africa | |||
| Global Climate Models |
| [91] |
| SWIM (Soil and water integrated model) |
| [99] |
| Sequential Maximum A Posteriori Classifier’ (SMAPC) |
| [100] |
| MAgPIE (model of agricultural production and its impact on world) |
| [101] |
| Water Poverty mapping |
| [102] |
Southeastern Asia | |||
| PROMET (Processes of Radiation, Mass and Energy transfer) and SURGES (Subscale Regional Glacier Extension Simulator) |
| [103] |
| MAgPIE (model of agricultural production and its impact on world) |
| [101] |
| Review of various documents |
| [104] |
Indonesia | Water sustainability hierarchy model |
| [105] |
Southern Asia | |||
| Assessment of blue water scarcity globally on a monthly basis |
| [106] |
Literature review of various policies and documents |
| [107] | |
| Temporal analysis and spatial mapping |
| [108] |
| GCAM (Global Change Assessment Model) |
| [109] |
| Catastrophe-theory based multi-criteria decision |
| [110] |
China | |||
LCA (Life cycle assessment) |
| [111] | |
MRIO (Multi-Regional input-output model) |
| [112] | |
MRIO (Multi-Regional input-output model) |
| [113] |
Regions or Countries | Model/Methodology | Major Findings | References |
---|---|---|---|
South America | |||
| Earth system model |
| [117] |
CLM (community land model) |
| [118] | |
Global Standardized Precipitation Evapotranspiration Index dataset |
| [119] | |
Agricultural drought hazard index (DHI) |
| [120] | |
Europe | |||
| CERES-Wheat crop-growth simulation model |
| [121] |
| Different crop models and pest models |
| [122] |
| Systematic review and meta-analysis of data |
| [123] |
RCM (Regional Climate Models) |
| [124] | |
Africa | |||
| Crop Models |
| [125] |
CMIP5 climate models and two crop models (SARRA-H and APSIM) |
| [126] | |
| CLM (community land model) |
| [118] |
Systematic review and meta-analysis of data |
| [127] | |
| Random Forest (a diagnostic machine learning technique) |
| [128] |
Southeastern Asia | |||
| Model of Shierary rice with Geographical Information system (GIS) |
| [129] |
Aqua crop |
| [130] | |
| (SPEI) Standardized Precipitation and Evapotranspiration Index |
| [131] |
| Food security Index and food decomposition method |
| [132] |
Southern Asia | |||
| Systematic review and meta-analysis of data |
| [127] |
Earth system model |
| [117] | |
| Stage stochastic production frontier (SPF) models, second-stage ordinary least square (OLS) and quantile regression models |
| [133] |
| Food security Index and food decomposition method |
| [132] |
| Decision Support System for Agrotechnology Transfer (DSSAT) |
| [134] |
China | |||
Study of different regions and crops and collection of data from China’s Agricultural statistics |
| [135] | |
CERES crop models along with the IPCC SRES A2 and B2 scenarios considering CO2 fertilization effect |
| [136] | |
Five global climate models and A1F1 and B1 (greenhouse gases emission scenarios) |
| [137] | |
Global Climate Model (GCM) and WheatGrow Model |
| [138] |
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Share and Cite
Saxena, R.; Vanga, S.K.; Wang, J.; Orsat, V.; Raghavan, V. Millets for Food Security in the Context of Climate Change: A Review. Sustainability 2018, 10, 2228. https://doi.org/10.3390/su10072228
Saxena R, Vanga SK, Wang J, Orsat V, Raghavan V. Millets for Food Security in the Context of Climate Change: A Review. Sustainability. 2018; 10(7):2228. https://doi.org/10.3390/su10072228
Chicago/Turabian StyleSaxena, Rachit, Sai Kranthi Vanga, Jin Wang, Valérie Orsat, and Vijaya Raghavan. 2018. "Millets for Food Security in the Context of Climate Change: A Review" Sustainability 10, no. 7: 2228. https://doi.org/10.3390/su10072228
APA StyleSaxena, R., Vanga, S. K., Wang, J., Orsat, V., & Raghavan, V. (2018). Millets for Food Security in the Context of Climate Change: A Review. Sustainability, 10(7), 2228. https://doi.org/10.3390/su10072228