International Crops Research Institute for the Semi-Arid Tropics
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Towards Lower Greenhouse Gas Emissions Agriculture in North Africa through Climate-Smart Agriculture: A Systematic Review
Get PDFNorth Africa (NA) is supposed to lower emissions in its agriculture to honor climate action commitments and to impulse sustainable development across Africa. Agriculture in North Africa has many assets and challenges that make it fit to use the tools of Climate-Smart Agriculture (CSA) for mitigation purposes. This study represents a first attempt to understand if CSA practices are sufficiently established in NA to contribute to reducing agriculture emissions. A PRISMA-inspired systematic review was carried out on an initial 147 studies retrieved from Scopus, Google Scholar, and the Web of Science databases, as well as from gray literature. 11 studies were included in the final analysis since they report the mitigation and co-benefits of CSA-based practices within NA. A bias risk was identified around the optimal inclusion of studies produced in French, and a specific plan was set for its minimization. Synthesis results revealed that most studies focused either on improving soil quality (nine studies) or managing enteric fermentation (two studies). The review revealed a poor establishment of the CSA framework in the region, especially in sequestering GHG emissions. A set of recommendations has been formulated to address the identified gaps from research orientations and organizational perspectives and empower the CSA as an ally for mitigation in north African agriculture
Evaluating the trade-offs and sustainability of minimum tillage and crop-residue addition for food production in the dryland tropics
No full textA long-term study was carried out at ICRISAT farm, Patancheru (India) during rainy (kharif) season 2009–18 with an objective to evaluate impact of tillage (minimum/conventional) and residue (addition/removal) management practices on production capacity and sustainability of maize-chickpea sequential and maize+pigeonpea intercropping systems. The experiment was laid out in spilt plot design with 4 replications. The effects of residue addition were inconsistent till the third year of experiment. However, yield benefits from residue addition became apparent fourth year onwards. After 7 years, residue addition showed significant yield benefits to the tune of 9–25% during 2016–17 (8th cropping cycle) and 18–31% during 2017–18 (9th cropping cycle) in both cropping systems. Residue addition also resulted in additional net income of `16,900/ha to `22,980/ha compared to no-residue addition. Minimum tillage in general tended to result in yield loss over normal tillage since beginning. During 8th and 9th cropping cycles, there was net annual loss up to `5400/ha under minimum tillage. Alongside productivity and economic benefits, at the end of 8th year i.e. 2016–17, residue addition showed improved soil health and sequestered around 300 kg carbon/ha/year
Genome-Wide Association Analysis Reveals Trait-Linked Markers for Grain Nutrient and Agronomic Traits in Diverse Set of Chickpea Germplasm
Get PDFChickpea is an inexpensive source of protein, minerals, and vitamins to the poor people living in arid and semi-arid regions of Southern Asia and Sub-Saharan Africa. New chickpea cultivars with enhanced levels of protein, Fe and Zn content are a medium-term strategy for supplying essential nutrients for human health and reducing malnutrition. In the current study, a chickpea reference set of 280 accessions, including landraces, breeding lines, and advanced cultivars, was evaluated for grain protein, Fe, Zn content and agronomic traits over two seasons. Using a mid-density 5k SNP array, 4603 highly informative SNPs distributed across the chickpea genome were used for GWAS analysis. Population structure analysis revealed three subpopulations (K = 3). Linkage disequilibrium (LD) was extensive, and LD decay was relatively low. A total of 20 and 46 marker-trait associations (MTAs) were identified for grain nutrient and agronomic traits, respectively, using FarmCPU and BLINK models. Of which seven SNPs for grain protein, twelve for Fe, and one for Zn content were distributed on chromosomes 1, 4, 6, and 7. The marker S4_4477846 on chr4 was found to be co-associated with grain protein over seasons. The markers S1_11613376 and S1_2772537 co-associated with grain Fe content under NSII and pooled seasons and S7_9379786 marker under NSI and pooled seasons. The markers S4_31996956 co-associated with grain Fe and days to maturity. SNP annotation of associated markers were found to be related to gene functions of metal ion binding, transporters, protein kinases, transcription factors, and many more functions involved in plant metabolism along with Fe and protein homeostasis. The identified significant MTAs has potential use in marker-assisted selection for developing nutrient-rich chickpea cultivars after validation in the breeding populations
Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India
No full textThe increasing availability of complex, geo-referenced on-farm data demands analytical frameworks that can guide crop management recommendations. Recent developments in interpretable machine learning techniques offer opportunities to use these methods in agronomic studies. Our objectives were two-fold: (1) to assess the performance of different machine learning methods to explain on-farm wheat yield variability in the Northwestern Indo-Gangetic Plains of India, and (2) to identify the most important drivers and interactions explaining wheat yield variability. A suite of fine-tuned machine learning models (ridge and lasso regression, classification and regression trees, k-nearest neighbor, support vector machines, gradient boosting, extreme gradient boosting, and random forest) were statistically compared using the R2, root mean square error (RMSE), and mean absolute error (MAE). The best performing model was again fine-tuned using a grid search approach for the bias-variance trade-off. Three post-hoc model agnostic techniques were used to interpret the best performing model: variable importance (a variable was considered “important” if shuffling its values increased or decreased the model error considerably), interaction strength (based on Friedman’s H-statistic), and two-way interaction (i.e., how much of the total variability in wheat yield was explained by a particular two-way interaction). Model outputs were compared against empirical data to contextualize results and provide a blueprint for future analysis in other production systems. Tree-based and decision boundary-based methods outperformed regression-based methods in explaining wheat yield variability. Random forest was the best performing method in terms of goodness-of-fit and model precision and accuracy with RMSE, MAE, and R2 ranging between 367 and 470 kg ha−1, 276–345 kg ha−1, and 0.44–0.63, respectively. Random forest was then used for selection of important variables and interactions. The most important management variables explaining wheat yield variability were nitrogen application rate and crop residue management, whereas the average of monthly cumulative solar radiation during February and March (coinciding with reproductive phase of wheat) was the most important biophysical variable. The effect size of these variables on wheat yield ranged between 227 kg ha−1 for nitrogen application rate to 372 kg ha−1 for cumulative solar radiation during February and March. The effect of important interactions on wheat yield was detected in the data namely the interaction between crop residue management and disease management and, nitrogen application rate and seeding rate. For instance, farmers’ fields with moderate disease incidence yielded 750 kg ha−1 less when crop residues were removed than when crop residues were retained. Similarly, wheat yield response to residue retention was higher under low seed and N application rates. As an inductive research approach, the appropriate application of interpretable machine learning methods can be used to extract agronomically actionable information from large-scale farmer field data
Evaluation of Salicornia bigelovii Germplasm for Food Use in Egypt and the United Arab Emirates Based on Agronomic Traits and Nutritional Composition
Get PDFClimate change significantly aggravates the quality of soil and water, especially in desert regions such as the United Arab Emirates (UAE) and Egypt concluding in an alarming increase in salinity in the reservoirs of the natural resources. Saline farming rises as a promising solution,
utilizing low-quality water and land resources to grow salt-tolerant varieties of conventional crops and halophytes. Samphire (Salicornia spp.) is among the most popular multi-purpose halophytes that are locally consumed in several countries around the world as a vegetable. Six Salicornia bigelovii genotypes (ICBA-2, ICBA-3, ICBA-4, ICBA-8, ICBA-9, ICBA-10) were evaluated for their agronomic performance and nutritional composition in Dubai in UAE and, for the first time, at the Red Sea Governorate in Egypt in the 2019–2020 season using saline groundwater for irrigation (ECw = 26
and 6.6 dS/m, respectively). ICBA-10 performed well in both locations with high green biomass and seed yield (10.9 kgm-2 and 116.3 gm-2, respectively, in UAE; 7.7 kgm-2 and 82.9 gm-2, respectively, in Egypt). ICBA-10 was, overall, also good in ion accumulation, total amino acids and unsaturated
fatty acids content in both locations for shoots and seeds. Our results indicated that a lack of a drainage system and leaching fraction, the silt loam texture and the drip irrigation system might have contributed in the gradual accumulation of salts in the soil at Mubarak Valley at the end of the experiment at a higher level than ICBA. Apart from the agronomic parameters, higher salinity levels also affected ion accumulation, the amino acids and the fatty acids content for both shoots and seeds, whereas the proximate composition was affected to a lesser extent. Our findings on the high unsaturated fatty acids content under higher salinity corroborate the nutritional value of S. bigelovii oil. Due to its euhalophyte nature, S. bigelovii is a valuable source of minerals, amino acids and
antioxidants that render it the most promising salt-loving plant for food use
Photosynthetic microbes in nanobiotechnology: Applications and perspectives
No full textPhotosynthetic microbes like brown algae, red algae, green-algae and blue-green algae (cyanobacteria) are utilized extensively for various commercial and industrial purposes. However, in recent time, their application has shifted to nanotechnology. The synthesis of metal nanoparticles using algal resources is known as Phyconanotechnology. Due to various advantages of the photosynthetic microbes such as presence of bioactive molecules, scalability, high metal uptake and cultivability, these microbes form ideal sources for nanoparticle synthesis. The green synthesis of nanoparticles is a non-toxic and environment-friendly alternative compared to other hazardous chemical and physical routes of synthesis. Several species of algae are explored for the fabrication of metal and metal oxide nanoparticles. Various physical characterization techniques collectively contribute in defining the surface morphology of nanoparticles and the existing functional groups for bioreduction and stability. A wide range of nanostructured metals like gold, silver, copper, zinc, iron, platinum and palladium are fabricated using algae and cyanobacteria. Due to the unique properties of the phycogenic nanoparticles, biocompatibility and safety aspects, all of these metal nanoparticles have their applications in facets like infection control, diagnosis, drug delivery, biosensing and bioremediation. Herein, the uniqueness of the phycogenic nanoparticles along with their distinctive antibacterial, antifungal, antibiofilm, algaecidal, antiviral, anticancer, antioxidant, antidiabetic, dye degradation, metal removal and catalytic properties are featured. Lastly, this work highlights the various challenges and future perspectives for further exploration of the biogenic metal nanoparticles for development of nanomedicine and environmental remediation in the coming years
Estimating the potential to close yield gaps through increased efficiency of chickpea production in Ethiopia
No full textImproved cultivars and agronomic practices have significantly increased chickpea production in Ethiopia in recent decades. Enhanced availability of chickpeas in Ethiopia, therefore, contributes to food, nutrition, and income security of the country. However, we know relatively little about the extent to which farmers have harnessed the full potential of these improved technologies. In this paper, we compare the technical efficiencies and technological gap ratios of chickpea farming in three major chickpea-producing areas of Ethiopia using a two-step meta frontier model. Based on regionally representative data from 681 chickpea-growing farm households in the three regions, we show regional differences in the technical efficiencies, technological gap ratios, and meta technical efficiencies (MTEs). We examined the drivers of these different production levels and identified ways to increase chickpea production while minimizing yield gaps. Improving technical efficiency through improving farmers’ access to improved seed, offering farmers need-based and gender-responsive extension support, encouraging their participation in technology development programs, and appropriate rainwater management would all contribute to harnessing the full potential of improved chickpea cultivars in Ethiopia
State of ex situ conservation of landrace groups of 25 major crops
Get PDFCrop landraces have unique local agroecological and societal functions and offer important genetic resources for plant breeding. Recognition of the value of landrace diversity and concern about its erosion on farms have led to sustained efforts to establish ex situ collections worldwide. The degree to which these efforts have succeeded in conserving landraces has not been comprehensively assessed. Here we modelled the potential distributions of eco-geographically distinguishable groups of landraces of 25 cereal, pulse and starchy root/tuber/fruit crops within their geographic regions of diversity. We then analysed the extent to which these landrace groups are represented in genebank collections, using geographic and ecological coverage metrics as a proxy for genetic diversity. We find that ex situ conservation of landrace groups is currently moderately comprehensive on average, with substantial variation among crops; a mean of 63% ± 12.6% of distributions is currently represented in genebanks. Breadfruit, bananas and plantains, lentils, common beans, chickpeas, barley and bread wheat landrace groups are among the most fully represented, whereas the largest conservation gaps persist for pearl millet, yams, finger millet, groundnut, potatoes and peas. Geographic regions prioritized for further collection of landrace groups for ex situ conservation include South Asia, the Mediterranean and West Asia, Mesoamerica, sub-Saharan Africa, the Andean mountains of South America and Central to East Asia. With further progress to fill these gaps, a high degree of representation of landrace group diversity in genebanks is feasible globally, thus fulfilling international targets for their ex situ conservation
Traditional Rainwater Management (Haveli cultivation) for Building System Level Resilience in a Fragile Ecosystem of Bundelkhand Region, Central India
Get PDFThis article presents the evidence on how the traditional rainwater management system (haveli system) has contributed toward rehabilitating degraded landscapes and changing them into a productive form in Bundelkhand region of Central India. The haveli system was the lifeline of the region for water security for the last 300 years. Farmers (~1–5%) situated at the upstream of the landscape were harvesting surface runoff in their fields during monsoon by constructing earthen embankments along with provision to drain out water after receding of the monsoon. Farmers traditionally cultivated only during the post-monsoon period, using residual soil moisture along with supplemental irrigation from shallow dug wells. However, this system became defunct due to apathy and poor maintenance. The traditional design of the havelis were also often malfunctioning due to new rainfall patterns and storm events. Farmers are facing new need for haveli rejuvenation and the traditional design and knowledge calls for new innovations, particularly from research and external expertise. In this context, ICRISAT and consortium partners have introduced an innovative approach for haveli rejuvenation by constructing masonry core wall along with outlet at a suitable location. Totally 40 haveli structures were constructed between 2010 and 2021 across seven districts of Bundelkhand region. One of the pilot sites (i.e., Parasai-Sindh) was intensively monitored in order to capture the landscape hydrology, change in land use, cropping intensity and crop productivity, between 2011 and 2017. Out of 750 mm rainfall received during July and September, generated surface runoff is about 135 mm (18% of rainfall) on average. However, rainfall below 450 mm (dry years) rarely generates surplus water as most of the rainfall received in such years are absorbed within the vadose zone, whereas, wet years with over 900 mm rainfall, generate runoff of about 250–300 mm (~30–35%). Rejuvenation of the haveli system created an opportunity to harvest surface runoff within farmers' fields which helped to improve groundwater levels in shallow dug wells (additionally by 2–5 m hydraulic head) which remained available during the following years. This has increased cropping intensity—by converting about 20% of permanent fallow lands into productive agriculture lands—and ensured irrigation availability especially during the critical crop growth stage. This enhanced land and water use efficiency of the system and increased household net income by two to three folds as compared to the baseline status. This article further establishes the link between landscape rejuvenation through haveli system, groundwater resource availability, production system and household income in the fragile ecosystem of Central India. The results are helpful for various stakeholders so that they can take informed decisions on sustainable natural resource management
Current Research Trends and Prospects for Yield and Quality Improvement in Sesame, an Important Oilseed Crop
Get PDFClimate change is shifting agricultural production, which could impact the economic and cultural contexts of the oilseed industry, including sesame. Environmental threats (biotic and abiotic stresses) affect sesame production and thus yield (especially oil content). However, few studies have investigated the genetic enhancement, quality improvement, or the underlying mechanisms of stress tolerance in sesame. This study reveals the challenges faced by farmers/researchers growing sesame crops and the potential genetic and genomic resources for addressing the threats, including: (1) developing sesame varieties that tolerate phyllody, root rot disease, and waterlogging; (2) investigating beneficial agro-morphological traits, such as determinate growth, prostrate habit, and delayed response to seed shattering; (3) using wild relatives of sesame for wide hybridization; and (4) advancing existing strategies to maintain sesame production under changing climatic conditions. Future research programs need to add technologies and develop the best research strategies for economic and sustainable development