CIMMYT Journal Articles

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Q&A: Methods for estimating genetic gain in sub-Saharan Africa and achieving improved gains
(Journal Article, 2024-06) Dieng, Ibnou; Gardunia, Brian; Covarrubias‐Pazaran, Giovanny; Gemenet, Dorcus C.; Trognitz, Bodo; Ofodile, Sam; Fowobaje, Kayode; Ntukidem, Solomon; Shah, Trushar; Imoro, Simon; Tripathi, L.; Mushoriwa, Hapson; Mbabazi, Ruth; Salvo, Stella; Derera, John
Regular measurement of realized genetic gain allows plant breeders to assess and review the effectiveness of their strategies, allocate resources efficiently, and make informed decisions throughout the breeding process. Realized genetic gain estimation requires separating genetic trends from nongenetic trends using the linear mixed model (LMM) on historical multi‐environment trial data. The LMM, accounting for the year effect, experimental designs, and heterogeneous residual variances, estimates best linear unbiased estimators of genotypes and regresses them on their years of origin. An illustrative example of estimating realized genetic gain was provided by analyzing historical data on fresh cassava (Manihot esculenta Crantz) yield in West Africa (https://github.com/Biometrics‐IITA/Estimating‐Realized‐Genetic‐Gain). This approach can serve as a model applicable to other crops and regions. Modernization of breeding programs is necessary to maximize the rate of genetic gain. This can be achieved by adopting genomics to enable faster breeding, accurate selection, and improved traits through genomic selection and gene editing. Tracking operational costs, establishing robust, digitalized data management and analytics systems, and developing effective varietal selection processes based on customer insights are also crucial for success. Capacity building and collaboration of breeding programs and institutions also play a significant role in accelerating genetic gains.
Risk-based evaluations of competing agronomic climate adaptation strategies: The case of rice planting strategies in the indo-Gangetic Plains
(Journal Article, 2024-06) Mkondiwa, Maxwell; Urfels, Anton
CONTEXT: Adjusting crop planting dates and variety durations is emerging as a crucial climate change adaptation strategy for many cereal systems. Such strategies include harmonizing crop planting with the onset of the rainy season or planting at specific recommended calendar dates. Evaluations of these strategies mostly consider yield and yield variability, but focus less on financial risks associated with different planting strategies and importance of risk aversion behaviour of the farmers in their decision to adopt the strategies. OBJECTIVE: Here, we present a novel framework that uses a computational spatial ex-ante approach for risk-based evaluations of agronomic adaptation options. This framework allows development agronomic adaptation recommendations that consider climate risks for risk-averse famrers. METHODS: We use a second order stochastic dominance approach that is paired with computational optimization—Golden section search algorithm. This approach allows a distributional assessment of risk and uncertainty by providing bounds at which even a risk averse would benefit from changing practices. This contrasts with conventional methods that do not consider farmers' risk aversion, e.g. mean-variance or conditional value at risk optimization methods. To demonstrate our approach, we compare the yield risks and economic risks associated with readily available gridded crop simulation outputs for various rice planting strategies across the Indo-Gangetic Plains (IGP)– a major region experiencing food insecurity and climate impacts. RESULTS AND CONCLUSIONS: The findings provide quantitative evidence about the riskiness of previously recommended rice planting date strategies. The risk-based assessment corroborates the recommendation for planting long-duration varieties at the monsoon onset with or without supplemental irrigation (covering about 22% of IGP area) in the Eastern IGP, and at state-recommended planting dates (covering about 38% of IGP area) in most of the Western and Middle IGP. Importantly, our risk-based assessment shows where the results are not as clear cut and which strategy is the least risky. This is especially important in the Middle IGP where farmers appear to have more flexibility to achieve comparable outcomes with several planting strategies. SIGNIFICANCE: In conclusion, the proposed approach provides a useful and novel tool for comparing different agronomic climate adaptation strategies from an economic risk perspective in a spatial framework.
Equity principles: Using social theory for more effective social transformation in agricultural research for development
(Journal Article, 2024-06) McGuire, Erin; Al-Zu'bi, Maha; Boa-Alvarado, Maria; Thi Thu Giang Luu; Sylvester, Janelle M.; Valencia Leñero, Eva Marina
CONTEXT: Agricultural innovations and their applications are increasingly recognized as crucial mechanisms for achieving the 2030 Sustainable Development Goals (SDGs). Actors in agricultural research for development (AR4D) frequently use Agricultural Innovation Systems (AIS) frameworks to comprehend the ecosystems within which innovations are developed and scaled. Given the SDGs' emphasis on social outcomes, a reflection on social diversity, power, and the integration of social theory into AIS and AR4D tools is crucial for addressing the nuances of social objectives. OBJECTIVE: This research critically evaluates AIS frameworks and AR4D tools through applying social theory to enhance social outcomes. We offer practical application through the development of “Equity Principles for Social Transformation (EPs).” These EPs are designed to guide AR4D organizations in innovation and scaling efforts that effectively achieve meaningful social outcomes. Through this approach, we aim to enrich the conceptual understanding of equity within AIS and provide practical strategies for implementing these insights, thus empowering AR4D actors to be more effective. METHODS: We start by selecting key social theories to analyze global power imbalances and local social exclusion within AIS frameworks and AR4D tools. Using these theories, we examine three case studies to uncover gaps in their approach to social dimensions. We categorize these gaps through thematic analysis and formulate EPs informed by social theories and a practical understanding of AR4D tools. RESULTS AND CONCLUSIONS: Equity analysis of each case study reveals gaps in understanding social implications within upstream and downstream research efforts. These gaps include insufficient addressing of power dynamics and agency recognition, lack of comprehensive guidance on critical social components, oversight of cultural and institutional norms, exacerbation of social inequities, and the case studies' limitations in flexibility for addressing social inclusion effectively. Additionally, there is a notable lack of clear operational guidelines for applying the frameworks in diverse contexts, including the challenge of translating conceptual levels into local action. Seven EPs were developed: recognize AR4D power dynamics; define goals, anti-goals, and for whom; build global “horizontal” partnerships; acknowledge social differences among innovation users and non-users; innovate and curate innovation appropriately; assess impact and reflect; and develop systems capacity. SIGNIFICANCE: The EPs connect innovation systems with positive social change. They help AR4D professionals consider and evaluate the impact of innovation. The EPs provide an additional framework that enables AR4D practitioners to prioritize user needs from the beginning, challenge biases, and more effectively achieve the social objectives outlined in the SDGs.
Alternative cropping and feeding options to enhance sustainability of mixed crop-livestock farms in Bangladesh
(Journal Article, 2023) Shahin Alam; Krupnik, Timothy J.; Shanjida Sharmin; Mohammad Ashiqul Islam; Groot, Jeroen C.J.
We investigated alternative cropping and feeding options for large (>10 cows), medium (5–10 cows) and small (≤4 cows) mixed crop – livestock farm types, to enhance economic and environmental performance in Jhenaidha and Meherpur districts – locations with increasing dairy production – in south western Bangladesh. Following focus group discussions with farmers on constraints and opportunities, we collected baseline data from one representative farm from each farm size class per district (six in total) to parameterize the whole-farm model FarmDESIGN. The six modelled farms were subjected to Pareto-based multi-objective (differential evolution algorithm) optimization to generate alternative dairy farm and fodder configurations. The objectives were to maximize farm profit, soil organic matter balance, and feed self-reliance, in addition to minimizing feed costs and soil nitrogen losses as indicators of sustainability. The cropped areas of the six baseline farms ranged from 0.6 to 4.0 ha and milk production per cow was between 1,640 and 3,560 kg year−1. Feed self-reliance was low (17%–57%) and soil N losses were high (74–342 kg ha−1 year−1). Subsequent trade-off analysis showed that increasing profit and soil organic matter balance was associated with higher risks of N losses. However, we found opportunities to improve economic and environmental performance simultaneously. Feed self-reliance could be increased by intensifying cropping and substituting fallow periods with appropriate fodder crops. For the farm type with the largest opportunity space and room to manoeuvre, we identified four strategies. Three strategies could be economically and environmentally benign, showing different opportunities for farm development with locally available resources.
Why we need to go beyond technology
(Journal Article, 2023) Odjo, Sylvanus; Ostermann, Heike
Food loss and waste is a multifactorial phenomenon. Therefore, at least in the long term, one-dimensional efforts to mitigate it, such as providing storage technologies, will not prove successful, our authors maintain, and they call for a systemic approach.
Insights for climate change adaptation from early sowing of wheat in the Northern Indo-Gangetic Basin
(Journal Article, 2023) Paudel, Gokul Prasad; Chamberlin, Jordan; Singh, Balwinder; Maharjan, Shashish; Trung Thanh Nguyen; Craufurd, Peter Q.; McDonald, Andrew
Automated in-season rice crop mapping using Sentinel time-series data and Google Earth Engine: A case study in climate-risk prone Bangladesh
(Journal Article, 2024-02-01) Tiwari, Varun; Tulbure, Mirela G.; Caineta, Júlio; Gaines, Mollie D.; Perin, Vinicius; Kamal, Mustafa; Krupnik, Timothy J.; Md Abdullah Aziz; AFM Tariqul Islam
High-resolution mapping of rice fields is crucial for understanding and managing rice cultivation in countries like Bangladesh, particularly in the face of climate change. Rice is a vital crop, cultivated in small scale farms that contributes significantly to the economy and food security in Bangladesh. Accurate mapping can facilitate improved rice production, the development of sustainable agricultural management policies, and formulation of strategies for adapting to climatic risks. To address the need for timely and accurate rice mapping, we developed a framework specifically designed for the diverse environmental conditions in Bangladesh. We utilized Sentinel-1 and Sentinel-2 time-series data to identify transplantation and peak seasons and employed the multi-Otsu automatic thresholding approach to map rice during the peak season (April–May). We also compared the performance of a random forest (RF) classifier with the multi-Otsu approach using two different data combinations: D1, which utilizes data from the transplantation and peak seasons (D1 RF) and D2, which utilizes data from the transplantation to the harvest seasons (D2 RF). Our results demonstrated that the multi-Otsu approach achieved an overall classification accuracy (OCA) ranging from 61.18% to 94.43% across all crop zones. The D2 RF showed the highest mean OCA (92.15%) among the fourteen crop zones, followed by D1 RF (89.47%) and multi-Otsu (85.27%). Although the multi-Otsu approach had relatively lower OCA, it proved effective in accurately mapping rice areas prior to harvest, eliminating the need for training samples that can be challenging to obtain during the growing season. In-season rice area maps generated through this framework are crucial for timely decision-making regarding adaptive management in response to climatic stresses and forecasting area-wide productivity. The scalability of our framework across space and time makes it particularly suitable for addressing field data scarcity challenges in countries like Bangladesh and offers the potential for future operationalization.
Diversities In Motion: Multifunctionality of Maize Production in Different Family Farming Systems in South and Central Mexico
(Journal Article, 2023-12-19) Boué, C.; Zepeda Villarreal, Ernesto Adair; Martínez García, Gloria; López Ridaura, Santiago; Barba-Escoto, Luis; Camacho Villa, Tania Carolina
Background: Maize agricultural policy in Mexico has focused on a monofunctional vision of maize as a basic commercial product, through a bimodal vision of production systems (commercial and subsistence). However, the evidence suggests that the challenge of thinking about the multifunctionality of this crop must be faced due to the complexity of its relationship within different strata of society, to more adequately reflect the diversity of systems based on maize, as well as their flexibility to respond to new challenges and opportunities, and to have better public policy designs. Objective: This work seeks to delve into the importance of the multifunctionality of maize within the context of different types of production units in Central and Southern Mexico, which represent families that make use of different production systems based on maize. This diversity is not a simple cultural curiosity, but rather reflects the complex use of maize cultivation as an economic and cultural mechanism that provides stability to Mexican families who depend on maize as their main crop. Methodology: To describe the multifunctionality of maize in Mexico, we adopted a qualitative approach through in-depth interviews with 51 maize producers from different types of production unit (PU) in the states of Oaxaca, Chiapas, Mexico, and Puebla. A study of production units (PU) typologies carried out with information from 16 states of the country was taken as a basis, where five types of PU were characterized according to their available resources, maize management, and their social characteristics. Results: It was found that: (1) there is a clearly distinguishable PU gradient (where, in addition to the existence of commercial and subsistence units, three others were identified, with direct implications for the design of public policy) that use maize with several purposes; (2) multifunctionality is associated with the diversity of uses and genetic materials that PUs have, and; (3) the variety of functions of maize changes according to the importance of maize in each type of unit and trough time. Implications: This work is positioned in favor of an expanded vision of the maize sector in Mexico instead of a dichotomous vision, where maize systems behave as a fluid continuum where the context of the PU’s affects their relationship with maize, and the way in which they use this crop to face social, climatic, and economic changes, as well as their preferences as consumers, traditions, and cultural identities. Conclusions: This complexity calls to thinking about a pluridiverse maize policy that understands the social complexity of this crop through the multifunctional support it offers to different types of UP’s based on maize systems, and how these differences require more sophisticated institutional approaches. Key words: multifunctionality of agriculture; maize; farm typologies; diversification of agricultural activities; diversity of maize-based systems.
Influence of poverty concerns on demand for healthier processed foods: A field experiment in Mexico City
(Journal Article, 2023-04-01) Dominguez-Viera, Marcos E.; Berg, Marrit van den; Handgraaf, Michel; Donovan, J.
Living in poverty can present cognitive biases that exacerbate constraints to achieving healthier diets. Better diets could imply food choice upgrades within certain food categories, such as electing processed foods with an improved nutritional profile. This study evaluated the influence of monetary and health concerns on the willingness to pay (WTP) for healthier processed foods in a low-income section of Mexico City. We employed priming techniques from the scarcity literature, which are applied for the first time to healthier food purchasing behaviours in low-income settings. Our predictions are based on a dual system framework, with choices resulting from the interaction of deliberative and affective aspects. The WTP was elicited through a BDM mechanism with 423 participants. Results showed that induced poverty concerns reduced the valuations of one of the study’s healthier food varieties by 0.17 standard deviations. The latter effect did not differ by income level. The WTP for a healthier bread product but one with relatively high sugar and fat content was reduced by induced poverty concerns only among certain consumers without bread purchasing restrictions (78% of the sample). Potential mechanisms were assessed through regression analysis and structural equation modelling. The relationship between poverty concerns and WTP was mediated by increased levels of stress. While we could not rule out impact on cognitive load, it was not deemed a mediator in this study. Our findings signal that improvements in economic and psychological well-being among low-income consumers may aid to increase their demand for healthier processed foods.
Enhancing maize yield in a conservation agriculture-based maize (Zea mays)- wheat (Triticum aestivum) system through efficient nitrogen management
(Journal Article, 2023) Kumar, Kamlesh; Parihar, Chiter Mal; Nayak, Hiranmay S.; Godara, Samarth; Avinash, G.; Patra, Kiranmoy; Sena, Dipaka Ranjan; Reddy, Kedharnath Srikanth; Das, T.K.; Jat, S.L.; Gathala, Mahesh Kumar; Singh, Upendra; Sharawat, Y.S.
This study evaluated the impact of contrasting tillage and nitrogen management options on the growth, yield attributes, and yield of maize (Zea mays L.) in a conservation agriculture (CA)-based maize-wheat (Triticum aestivum L.) system. The field experiment was conducted during the rainy (kharif) seasons of 2020 and 2021 at the research farm of ICAR-Indian Agricultural Research Institute (IARI), New Delhi. The experiment was conducted in a split plot design with three tillage practices [conventional tillage with residue (CT), zero tillage with residue (ZT) and permanent beds with residue (PB)] as main plot treatments and in sub-plots five nitrogen management options [Control (without N fertilization), recommended dose of N @150 kg N/ha, Green Seeker-GS based application of split applied N, N applied as basal through urea super granules-USG + GS based application and 100% basal application of slow release fertilizer (SRF) @150 kg N/ha] with three replications. Results showed that both tillage and nitrogen management options had a significant impact on maize growth, yield attributes, and yield in both seasons. However, time to anthesis and physiological maturity were not significantly affected. Yield attributes were highest in the permanent beds and zero tillage plots, with similar numbers of grains per cob (486.1 and 468.6). The highest leaf area index (LAI) at 60 DAP was observed in PB (5.79), followed by ZT(5.68) and the lowest was recorded in CT (5.25) plots. The highest grain yield (2-year mean basis) was recorded with permanent beds plots (5516 kg/ha), while the lowest was observed with conventional tillage (4931 kg/ha). Therefore, the study highlights the importance of CA practices for improving maize growth and yield, and suggests that farmers can achieve better results through the adoption of CA-based permanent beds and use of USG as nitrogen management option.
Changes in soil organic carbon pools after 15 years of Conservation Agriculture in rice (Oryza sativa)-wheat (Triticum aestivum) cropping system of eastern Indo-Gangetic plains
(Journal Article, 2023-06) Mahala, Deep Mohan; Meena, Mahesh Chand; Dwivedi, Brahma S.; Datta, Siba Prasad; Dey, Abir; Das, Debarup; Parihar, Chiter Mal; Yadav, R.K.; Chaudhary, Amresh; Jat, Raj Kumar; Choudhary, Kajod Mal; Gathala, Mahesh Kumar; Jat, Mangi L.
The present study was carried out at Dr. Rajendra Prasad Central Agricultural University, Samastipur, Bihar during 2021-2023 to focus on examining alterations in SOC pools resulting from conservation agriculture (CA) practices in R-W system in the eastern IGP, following the collection of soil samples from a long-term trial that was initiated in rainy (kharif) season 2006. The trial included eight combinations, namely: conventional tilled rice (Oryza sativa L.) and wheat (Triticum aestivum L.) (CTR-CTW); CT rice and zero till wheat (CTR-ZTW); direct seeded rice (DSR) and wheat on permanent raised beds (PBDSR-PBW); ZTDSR and CT Wheat (ZTDSR-CTW); ZTDSR and ZT wheat without residue (ZTDSR-ZTW-R); ZTDSR-ZT wheat with residue (ZTDSR-ZTW +R); unpuddled transplanted riceZTW (UpTR-ZTW) and ZTDSR-sesbania brown manure-ZTW (ZTDSR-S-ZTW). Results revealed that implementing zero tillage (ZT) combined with residue retention in rice and wheat cultivation led to enhanced levels of soil organic carbon (SOC) across all four fractions, namely very labile (CVL), labile (CL), less labile (CLL), and non-labile (CNL), in comparison to the continuous and rotational tillage practices. The tillage and residue management options significantly affected the lability index (LI) and C pool index (CPI), with zero-tillage and residue retention leading to lower LI and higher CPI values. The management practices significantly affected the C management index (CMI), with zero-tillage and residue retention showing the highest CMI values. Findings showed the potential of CA practices for enhancing soil C quality as well as C sequestration in soil of the Eastern IGP of India.
El Niño’s effects on southern African agriculture in 2023/24 and anticipatory action strategies to reduce the impacts in Zimbabwe
(Journal Article, 2023-11-16) Mugiyo, H.; Magadzire, T.; Choruma, Dennis Junior; Chimonyo, Vimbayi Grace Petrova; Manzou, R.; Jiri, O.; Mabhaudhi, Tafadzwanashe
The frequency of El Niño occurrences in southern Africa surpasses the norm, resulting in erratic weather patterns that significantly impact food security, particularly in Zimbabwe. The effects of these weather patterns posit that El Niño occurrences have contributed to the diminished maize yields. The objective is to give guidelines to policymakers, researchers, and agricultural stakeholders for taking proactive actions to address the immediate and lasting impacts of El Niño and enhance the resilience of the agricultural industry. This brief paper provides prospective strategies for farmers to anticipate and counteract the El Niño-influenced dry season projected for 2023/24 and beyond. The coefficient of determination R2 between yield and ENSO was low; 11 of the 13 El Niño seasons had a negative detrended yield anomaly, indicating the strong association between El Nino’s effects and the reduced maize yields in Zimbabwe. The R2 between the Oceanic Nino Index (ONI) and rainfall (43%) and between rainfall and yield (39%) indirectly affects the association between ONI and yield. To safeguard farmers’ livelihoods and improve their preparedness for droughts in future agricultural seasons, this paper proposes a set of strategic, tactical, and operational decision-making guidelines that the agriculture industry should follow. The importance of equipping farmers with weather and climate information and guidance on drought and heat stress was underscored, encompassing strategies such as planting resilient crop varieties, choosing resilient livestock, and implementing adequate fire safety measures.
Opportunities to close wheat yield gaps in Nepal’s Terai: Insights from field surveys, on-farm experiments, and simulation modeling
(Journal Article, 2024-01-01) Devkota Wasti, Mina; Devkota, Krishna; Paudel, Gokul Prasad; Krupnik, Timothy; James McDonald, Andrew
CONTEXT Wheat (Triticum aestivum) is among the most important staple food crops in the lowland Terai region of Nepal. However, national production has not matched the increasing demand. From a South Asian regional perspective, average productivity is low with high spatial and temporal variability. OBJECTIVES This study determines entry points for closing yield gaps using multiple diagnostic approaches, i.e., field surveys, on-farm experiments, and simulation models across different wheat production environments in the Terai region of Nepal. METHODOLOGY Yield and production practice data were collected from 1745 wheat farmers' fields and analysed in tandem with over 100 on-farm experiments. These were complemented by long-term simulation modeling using the APSIM Next Generation to assess system production behavior over a range of climate years. RESULTS AND DISCUSSION On-farm survey data suggests that yield and profit gaps under farmers' management (difference between the most productive (top 10th decile) and average wheat fields) were 1.60 t ha−1 and 348 USD ha−1 in the Terai region. The potential yield gap (difference between simulated potential yield and surveyed population mean) estimated was 4.63 t ha−1, suggesting ample room for growth even for the highest-yielding fields. Machine learning diagnostics of survey data, and on-farm trials identified nitrogen rate, irrigation management, terminal heat stress, use of improved varieties, seeding date, seeding method, and seeding rate as the principal agronomic drivers of wheat yield. While fields in the top 10th decile yield distribution had higher fertilizer use efficiencies and irrigation and seeding rates with similar overall production costs as average-yielding farmers. Our results suggest a complementary set of agronomic interventions to increase wheat productivity among lower-yielding farms in the Terai including advancing the time of seeding by 7–10 days on average, increasing nitrogen fertilizer by 20 kg ha−1, and alleviating water stress by applying two additional irrigations. SIGNIFICANCE Although wheat yields in the Terai are among the lowest in the region, biophysical production potential is high and remains largely untapped due to sub-optimal agronomic management practices rather than intrinsic agroecological factors. Data from this study suggests that incremental changes in these practices may result in substantial gains in productivity and profitability.
High spatial resolution seasonal crop yield forecasting for heterogeneous maize environments in Oromia, Ethiopia
(Journal Article, 2023-11) Tesfaye, Kindie; Takele, Robel; Shelia, Vakhtang; Lemma, Esayas; Dabale, Addisu; Sibiry Traoré, Pierre C.; Solomon, Dawit; Hoogenboom, Gerrit
Seasonal climate variability determines crop productivity in Ethiopia, where rainfed smallholder farming systems dominate in the agriculture production. Under such conditions, a functional and granular spatial yield forecasting system could provide risk management options for farmers and agricultural and policy experts, leading to greater economic and social benefits under highly variable environmental conditions. Yet, there are currently only a few forecasting systems to support early decision making for smallholder agriculture in developing countries such as Ethiopia. To address this challenge, a study was conducted to evaluate a seasonal crop yield forecast methodology implemented in the CCAFS Regional Agricultural Forecasting Toolbox (CRAFT). CRAFT is a software platform that can run pre-installed crop models and use the Climate Predictability Tool (CPT) to produce probabilistic crop yield forecasts with various lead times. Here we present data inputs, model calibration, evaluation, and yield forecast results, as well as limitations and assumptions made during forecasting maize yield. Simulations were conducted on a 0.083° or ∼ 10 km resolution grid using spatially variable soil, weather, maize hybrids, and crop management data as inputs for the Cropping System Model (CSM) of the Decision Support System for Agrotechnology Transfer (DSSAT). CRAFT combines gridded crop simulations and a multivariate statistical model to integrate the seasonal climate forecast for the crop yield forecasting. A statistical model was trained using 29 years (1991–2019) data on the Nino-3.4 Sea surface temperature anomalies (SSTA) as gridded predictors field and simulated maize yields as the predictand. After model calibration the regional aggregated hindcast simulation from 2015 to 2019 performed well (RMSE = 164 kg/ha). The yield forecasts in both the absolute and relative to the normal yield values were conducted for the 2020 season using different predictor fields and lead times from a grid cell to the national level. Yield forecast uncertainties were presented in terms of cumulative probability distributions. With reliable data and rigorous calibration, the study successfully demonstrated CRAFT’s ability and applicability in forecasting maize yield for smallholder farming systems. Future studies should re-evaluate and address the importance of the size of agricultural areas while comparing aggregated simulated yields with yield data collected from a fraction of the target area.
Silage maize as a potent candidate for sustainable animal husbandry development—perspectives and strategies for genetic enhancement
(Journal Article, 2023) Karnatam, Krishna Sai; Mythri, Bikkasani; Wajhat Un Nisa; Sharma, Heena; Kumar, Tarun; Rana, Prabhat; Vikal, Yogesh; Gowda, Manje; Dhillon, Baldev Singh; Sandhu, Surinder
Maize is recognized as the queen of cereals, with an ability to adapt to diverse agroecologies (from 58oN to 55oS latitude) and the highest genetic yield potential among cereals. Under contemporary conditions of global climate change, C4 maize crops offer resilience and sustainability to ensure food, nutritional security, and farmer livelihood. In the northwestern plains of India, maize is an important alternative to paddy for crop diversification in the wake of depleting water resources, reduced farm diversity, nutrient mining, and environmental pollution due to paddy straw burning. Owing to its quick growth, high biomass, good palatability, and absence of anti-nutritional components, maize is also one of the most nutritious non-legume green fodders. It is a high-energy, low-protein forage commonly used for dairy animals like cows and buffalos, often in combination with a complementary high-protein forage such as alfalfa. Maize is also preferred for silage over other fodders due to its softness, high starch content, and sufficient soluble sugars required for proper ensiling. With a rapid population increase in developing countries like China and India, there is an upsurge in meat consumption and, hence, the requirement for animal feed, which entails high usage of maize. The global maize silage market is projected to grow at a compound annual growth rate of 7.84% from 2021 to 2030. Factors such as increasing demand for sustainable and environment-friendly food sources coupled with rising health awareness are fueling this growth. With the dairy sector growing at about 4%–5% and the increasing shortage faced for fodder, demand for silage maize is expected to increase worldwide. The progress in improved mechanization for the provision of silage maize, reduced labor demand, lack of moisture-related marketing issues as associated with grain maize, early vacancy of farms for next crops, and easy and economical form of feed to sustain household dairy sector make maize silage a profitable venture. However, sustaining the profitability of this enterprise requires the development of hybrids specific for silage production. Little attention has yet been paid to breeding for a plant ideotype for silage with specific consideration of traits such as dry matter yield, nutrient yield, energy in organic matter, genetic architecture of cell wall components determining their digestibility, stalk standability, maturity span, and losses during ensiling. This review explores the available information on the underlying genetic mechanisms and gene/gene families impacting silage yield and quality. The trade-offs between yield and nutritive value in relation to crop duration are also discussed. Based on available genetic information on inheritance and molecular aspects, breeding strategies are proposed to develop maize ideotypes for silage for the development of sustainable animal husbandry.
Combination of linkage and association mapping with genomic prediction to infer QTL regions associated with gray leaf spot and northern corn leaf blight resistance in tropical maize
(Journal Article, 2023) Omondi, Dennis O.; Dida, Mathews M.; Berger, Dave K.; Beyene, Yoseph; Nsibo, David L.; Juma, Collins; Mahabaleswara, Suresh L.; Gowda, Manje
Among the diseases threatening maize production in Africa are gray leaf spot (GLS) caused by Cercospora zeina and northern corn leaf blight (NCLB) caused by Exserohilum turcicum. The two pathogens, which have high genetic diversity, reduce the photosynthesizing ability of susceptible genotypes and, hence, reduce the grain yield. To identify population-based quantitative trait loci (QTLs) for GLS and NCLB resistance, a biparental population of 230 lines derived from the tropical maize parents CML511 and CML546 and an association mapping panel of 239 tropical and sub-tropical inbred lines were phenotyped across multi-environments in western Kenya. Based on 1,264 high-quality polymorphic single-nucleotide polymorphisms (SNPs) in the biparental population, we identified 10 and 18 QTLs, which explained 64.2% and 64.9% of the total phenotypic variance for GLS and NCLB resistance, respectively. A major QTL for GLS, qGLS1_186 accounted for 15.2% of the phenotypic variance, while qNCLB3_50 explained the most phenotypic variance at 8.8% for NCLB resistance. Association mapping with 230,743 markers revealed 11 and 16 SNPs significantly associated with GLS and NCLB resistance, respectively. Several of the SNPs detected in the association panel were co-localized with QTLs identified in the biparental population, suggesting some consistent genomic regions across genetic backgrounds. These would be more relevant to use in field breeding to improve resistance to both diseases. Genomic prediction models trained on the biparental population data yielded average prediction accuracies of 0.66–0.75 for the disease traits when validated in the same population. Applying these prediction models to the association panel produced accuracies of 0.49 and 0.75 for GLS and NCLB, respectively. This research conducted in maize fields relevant to farmers in western Kenya has combined linkage and association mapping to identify new QTLs and confirm previous QTLs for GLS and NCLB resistance. Overall, our findings imply that genetic gain can be improved in maize breeding for resistance to multiple diseases including GLS and NCLB by using genomic selection.
Editorial : Model organisms in plant science : Maize
(Journal Article, 2023) Butron, Ana; Santiago, Rogelio; Gowda, Manje
Maize has been an organism of historical importance to all biologists as eminent researchers such as Beadle, Emerson, McClintock, Stadler and Rhoades made ground-breaking genetic discoveries in maize that hold true for all living organisms (Andorf et al., 2016). Nowadays, plant lignocellulose represents the world’s greatest repository of renewable energy amenable to conversion into liquid, and maize has become one of the preferred choices due to their high biomass yields, broad geographic adaptation, carbon sequestration potential and nutrient utilization (Courtial et al., 2013; van der Weijde et al.). Therefore, this Research Topic aimed (1) to put forward the importance of research focuses in maize as a model organism, presenting recent developments and important accomplishments in moving forward the study of plants, and (2) to shed light on the progress made in the past decade working with maize as an important crop used worldwide.
Multicriteria assessment of alternative cropping systems at farm level. A case with maize on family farms of South East Asia
(Journal Article, 2023) Lairez, Juliette; Jourdain, Damien; López Ridaura, Santiago; Syfongxay, Chanthaly; Affholder, François
CONTEXT: Integration of farms into markets with adoption of maize as a cash crop can significantly increase income of farms of the developing world. However, in some cases, the income generated may still be very low and maize production may also have strong negative environmental and social impacts. OBJECTIVE: Maize production in northern Laos is taken as a case to study how far can farms' performance be improved with improved crop management of maize with the following changes at field level: good timing and optimal soil preparation and sowing, allowing optimal crop establishment and low weed infestation. METHODS: We compared different farm types' performance on locally relevant criteria and indicators embodying the three pillars of sustainability (environmental, economic and social). An integrated assessment approach was combined with direct measurement of indicators in farmers' fields to assess eleven criteria of local farm sustainability. A bio-economic farm model was used for scenario assessment in which changes in crop management and the economic environment of farms were compared to present situation. The farm model was based on mathematical programming maximizing income under constraints related to i) household composition, initial cash and rice stocks and land type, and ii) seasonal balances of cash, labour and food. The crop management scenarios were built based on a diagnosis of the causes of variations in the agronomic and environmental performances of cropping systems, carried out in farmers' fields. RESULTS AND CONCLUSIONS: Our study showed that moderate changes in crop management on maize would improve substantially farm performance on 4 to 6 criteria out of the 11 assessed, depending on farm types. The improved crop management of maize had a high economic attractiveness for every farm type simulated (low, medium and high resource endowed farms) even at simulated production costs more than doubling current costs of farmers' practices. However, while an improvement of the systems performance was attained in terms of agricultural productivity, income generation, work and ease of work, herbicide leaching, improved soil quality and nitrogen balance, trade-offs were identified with other indicators such as erosion control and cash outflow needed at the beginning of the cropping season. SIGNIFICANCE: Using farm modelling for multicriteria assessment of current and improved maize cropping systems for contrasted farm types helped capture main opportunities and constraints on local farm sustainability, and assess the trade-offs that new options at field level may generate at farm level.
Soil moisture content and maize grain yield under conventional and conservation agriculture practices - results of short term field tests in liselo, Namibia
(Journal Article, 2023) Kudumo, L. P.; Itanna, F.; Thierfelder, Christian L.; Kambatuku, J.
This article focuses on the results from trials developed to monitor the short-term effects of conventionally tilled systems versus CA on soil quality and crop productivity under conditions of the major cropping systems in central, north-central and north-eastern regions of Namibia. Conventional tillage (CT), Minimum tillage (MT), Minimum tillage, mulch (MT-M), Minimum tillage, rotation (MT-R) and Minimum tillage, mulch and rotation (MT-MR) were the primary treatments tested. Significant differences (p≤0.000) among the treatments were observed in the 0-60 cm soil profiles where MT-M plots had the highest soil moisture content (39.8 mm, Standard Error of Mean 0.2815) over the study period. A significant difference (p=0.0206) in grain yield was observed in the second season with CT plots yielding the highest grain yield (3852.3 kg ha-1, standard error of mean 240.35). Results suggest that CA has the potential to increase water conservation and contribute to reduction of the risk of crop failure. Climate change driven degradation under conventional tillage necessitate alternative sustainable tillage methods. Conservation tillage methods and conservation agricultural practices that minimize soil disturbance while maintaining soil cover need to be adopted more locally as viable alternatives to conventional tillage.
DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants
(Journal Article, 2023) Wang, Kelin; Abid, Muhammad Ali; Awais Rasheed; Crossa, José; Hearne, Sarah Jane; Huihui Li
Genomic prediction is an effective way to accelerate the rate of agronomic trait improvement in plants. Traditional methods typically use linear regression models with clear assumptions; such methods are unable to capture the complex relationships between genotypes and phenotypes. Non-linear models (e.g., deep neural networks) have been proposed as a superior alternative to linear models because they can capture complex non-additive effects. Here we introduce a deep learning (DL) method, deep neural network genomic prediction (DNNGP), for integration of multi-omics data in plants. We trained DNNGP on four datasets and compared its performance with methods built with five classic models: genomic best linear unbiased prediction (GBLUP); two methods based on a machine learning (ML) framework, light gradient boosting machine (LightGBM) and support vector regression (SVR); and two methods based on a DL framework, deep learning genomic selection (DeepGS) and deep learning genome-wide association study (DLGWAS). DNNGP is novel in five ways. First, it can be applied to a variety of omics data to predict phenotypes. Second, the multilayered hierarchical structure of DNNGP dynamically learns features from raw data, avoiding overfitting and improving the convergence rate using a batch normalization layer and early stopping and rectified linear activation (rectified linear unit) functions. Third, when small datasets were used, DNNGP produced results that are competitive with results from the other five methods, showing greater prediction accuracy than the other methods when large-scale breeding data were used. Fourth, the computation time required by DNNGP was comparable with that of commonly used methods, up to 10 times faster than DeepGS. Fifth, hyperparameters can easily be batch tuned on a local machine. Compared with GBLUP, LightGBM, SVR, DeepGS and DLGWAS, DNNGP is superior to these existing widely used genomic selection (GS) methods. Moreover, DNNGP can generate robust assessments from diverse datasets, including omics data, and quickly incorporate complex and large datasets into usable models, making it a promising and practical approach for straightforward integration into existing GS platforms.

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