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Surface albedo and thermal radiation dynamics under conservation and conventional agriculture in subhumid Zimbabwe
No full textWhile conservation agriculture (CA) has been widely evaluated for its biogeochemical effects (e.g soil organic carbon sequestration and greenhouse gas emissions) for climate mitigation, its biogeophysical impacts related to changes in surface albedo remain understudied. This study assessed the biogeophysical effects of CA cropping systems with maize (Zea mays L.) in Zimbabwe. Measurements were conducted continuously over two cropping years at two long-term experiments with contrasting soil characteristics, on an abruptic Lixisol and on a xanthic Ferralsol. The dynamics of surface albedo, longwave radiation, leaf area index, soil moisture and temperature were monitored under three different treatments: conventional tillage (CT, tilled to ∼15 cm), no-tillage (NT) and no-tillage with mulch (NTM, 2.5 t DM ha⁻¹). Our results revealed that, on the Ferralsol, NT and NTM significantly (p < 0.05) increased mean annual albedo (0.17) relative to CT (0.16), resulting in a negative instantaneous radiative forcing (iRF) and indicating a net cooling effect. iRF was stronger in 2021/22 (NT: -0.83 ± 0.17 W m-2; NTM: -1.43 ± 0.7 W m-2) than in 2022/23 (NT: -0.43 ± 0.09 W m-2; NTM: -1.03 ± 0.21 W m-2). Conversely, on the Lixisol, while NT increased surface albedo (0.27 vs. CT: 0.24), NTM significantly reduced albedo (0.23), causing positive iRF (warming). iRF was -3.34 ± 0.69 W m-2 and -2.78 ± 0.77 W m-2 for NT in the first and second cropping year, respectively, and increased from 1.14 ± 0.21 W -2 (2021/22) to 2.77 ± 0.41 W m-2 (2022/23) under NTM. Overall, our results suggest that the soil background albedo is an important site characteristic that needs to be considered and demonstrates the importance of considering biogeophysical effects when promoting practices of CA for climate change mitigation
Soil carbon and nitrogen emissions under farmer managed conservation agriculture in Zimbabwe
No full textWorldwide agriculture operates under the threefold challenge of adapting to climate change and mitigating its effects while aiming for sustainable agricultural intensification to meet the food demands of a growing population. Conservation agriculture (CA), a combination of reduced tillage, diversified crop rotations, and mulching, claims to target all three challenges at the same time. However, major knowledge gaps regarding CA’s mitigation potential remain. This study used a mobile, closed chamber system to determine soilborne, greenhouse gas (GHG) emissions from rainfed, farmer-managed CA- and conventional agriculture (CONV), in northern Zimbabwe. Measurements were carried out in locations of contrasting soil fertility (Arenosols and Luvisols) and under contrasting environmental conditions (cold-dry, cold-moist, warm-dry, warm-moist). Additionally, a horizon-specific soil fractionation with consecutive soil carbon and nitrogen quantification was conducted. The GHG emissions from a total of 8 farms depended on soil temperature and moisture and tended to be higher in CONV fields, although differences were statistically not significant. Field emissions were highest under warm-moist conditions, which are prevailing for large parts of the growing season. Mean carbon dioxide (CO2) emissions from Luvisols were 3.0% lower in CA fields (583 mg CO2 m2 h−1) than under CONV (601 mg CO2 m2 h−1), respectively 7.6% lower in CA fields (464 mg CO2 m2 h−1) than under CONV (502 mg CO2 m2 h−1) in Arenosols. Conservation agriculture reduced mean nitrous oxide (N2O) emissions by 17.5% from 0.27 mg N2O m2 h−1 (CONV) to 0.23 mg N2O m2 h−1 (CA) in Luvisols and by 54.7% from 1.16 mg N2O m2 h−1 (CONV) to 0.53 mg N2O m2 h−1 (CA) in Arenosols. The upper soil horizons of Luvisols had higher concentrations of particulate- and mineral-associated organic matter compared with Arenosols and lower soil horizons but no differences were noted between management systems. Our data indicate that the mitigation effects of CA are highly site-specific and that CA management practices can have unexpected negative effects on GHG fluxes. The unimodal rainfall distribution with a long dry winter period of 7 months and recurrent dry spells in northern Zimbabwe may prevent a net carbon sequestration under CA management that would have occurred in the humid tropics
Nutritional and industrial quality assessment of Spanish durum wheat commercial cultivars
No full textBACKGROUND Durum wheat is the raw material used to produce pasta, and its price is determined by grain physical characteristics, gluten strength and semolina yellowness. Gluten strength is mainly determined by high- and low-molecular-weight glutenin subunits (HMW-GS and LMW-GS). Semolina yellowness is determined by loci that control carotenoid content and lipoxygenase activity. Arabinoxylans are the major dietary fibre component within the durum wheat endosperm. Twelve durum wheat cultivars were grown in five locations over two cropping seasons. The objectives of this study were to determine the variability in the aforementioned traits; to assess the influence of genotype, environment and their interaction; and to determine the allelic variation of the main genes associated with gluten strength and semolina yellowness. RESULTS Grain physical characteristics were mainly determined by the environment. However, the genotype exerted a strong influence on gluten strength, semolina yellowness and arabinoxylan content. There was wide variation in all traits, but arabinoxylan content was limited. For HMW-GS the most common alleles were Glu-A1c and Glu-B1b, while for LMW-GS they were GLU-A3a, GLU-B3a and GLU-B2a. Regarding carotenoid synthesis genes, Psy-A1l, Psy-B1o, Pds-B1b and TdZds-A1.1 were the most frequent alleles; while Lpx-A3 UC1113 and Lpx-B1.1a were predominant for lipoxygenase genes. CONCLUSIONS Although the best alleles for gluten quality and yellow colour are present, they are not combined in a single cultivar, which limits the maximisation of overall quality. This study also highlights the importance of searching for arabinoxylan donors due to the limited genetic variability for this trait in commercial durum wheat cultivars.6839-684
Field soil moisture content under conventional tillage and conservation agriculture practices in Liselo, Namibia
No full textThis article focuses on the results of the trials developed to monitor the short-term effects of conventionally tilled practices (CP) versus Conservation Agriculture (CA) on soil quality and crop productivity under conditions of the major cropping systems in central, north-central and north-eastern regions of Namibia. The objective of the trials was to test the hypotheses that (a) CA treated plots have a significant higher water infiltration and soil moisture content (b) the CA principles (minimum tillage, soil cover and crop rotation or intercropping) have a significant influence on soil moisture content eventually leading to greater crop productivity. Results from Liselo in the Zambezi region of Namibia on the effects of tillage methods on soil moisture content are as follows. Conventional mouldboard ploughing (CPa), Sub-soiling with a Magoye ripper (SS-M) and Manual tillage using Dibble stick with mulch (MDS-M) were some of the treatments tested among others. Tillage systems appeared to have significantly affected (P<0.05) soil moisture in the 0-30 and 0-60 cm soil depths over the study period. Plots subsoiled with Magoye Ripper (SSM) (14.9mm) had 3.47% higher average soil moisture content in the 0-30cm soil depth and 3.05% higher moisture in the 0-60 cm soil depth than conventional ploughing. Manual tilling with a dibble stick (MDS-M) and conventional tilling with a plough (CTa) were found to be insignificantly different from each other with soil moisture averages of 14.1 mm and 14.4 mm in the 0-30 cm soil depth, respectively, and 39.3 mm for both in the 0-60cm soil depth, respectively. Results suggest that some tillage methods and CA practices have the potential to increase water conservation and contribute to reduction of risk of crop failure, as was observed where subsoiled plots had more soil moisture content than conventionally tilled plots
Consumer acceptance of foods derived from blended wheat flour in Nairobi, Kenya
No full textGovernments across Africa have shown enthusiasm for wheat flour blending to reduce food security risks and pull demand for traditional but underutilized crops. However, research has sidestepped the question of whether consumers will accept foods derived from blended wheat flour. We used sensory evaluation and contingent valuation techniques with a sample of 1871 consumers in Nairobi, Kenya to measure the acceptance of two commonly consumed foods (chapati and bread) made from wheat flours blended with up to 20% sorghum, millet, or cassava flour. In blind tasting, bread made of blended flour was slightly less preferred than conventional bread, while chapati products made of wheat and sorghum (10%) or millet (5%) blends were equally valued as chapati made of 100% wheat flour, suggesting the potential to replace up to 10% of wheat flour in chapati without compromising sensory characteristics and consumer acceptance. When informed about the flour composition before tasting, consumers showed a stronger preference for the products made from blended flour and expressed a higher willingness to pay for blend-based products than conventional products. We discuss the policy implications of how consumer interest in such foods can be harnessed to advance food security and economic development goals
Wheat in Kenya: toward self-sufficiency or toward broader development goals
No full textThe conflict between Russia and Ukraine, and the associated disruptions in global wheat supply has resulted in concern for food security throughout sub-Saharan Africa. In Kenya, which depends heavily on wheat imports to meet demand, this concern has intensified calls for self-sufficiency in wheat production. Wheat shortages have led to price hikes that hit all consumers but the urban poor in particular. To decrease reliance on imports, for both food security and for nutrition, Kenya has implemented policy measures to spur increased wheat production. This paper explores the context for increasing wheat production in Kenya to respond to increasing demand, and for addressing the needs of the stakeholders in the sector. Findings suggest that wheat self-sufficiency is unlikely to be achieved soon. Major public and private investments would be required to build the infrastructure, systems, and institutions required to support smallholders to expand and intensify their production. Millers have relied on cheap wheat imports for decades and show limited signs of willingness to support backward linkages with farmers. Critical public infrastructure (e.g., wheat seed systems, extension systems) is ill-equipped to support the growth of the wheat sector. Researchers and policy makers would better serve the interests of smallholder wheat growers by identifying feasible objectives for sustainable and equitable industry growth. We conclude with recommendations for targeted investment and interventions
Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software
No full textWith growing evidence that genomic selection (GS) improves genetic gains in plant breeding, it is timely to review the key factors that improve its efficiency. In this feature review, we focus on the statistical machine learning (ML) methods and software that are democratizing GS methodology. We outline the principles of genomic-enabled prediction and discuss how statistical ML tools enhance GS efficiency with big data. Additionally, we examine various statistical ML tools developed in recent years for predicting traits across continuous, binary, categorical, and count phenotypes. We highlight the unique advantages of deep learning (DL) models used in genomic prediction (GP). Finally, we review software developed to democratize the use of GP models and recent data management tools that support the adoption of GS methodology.756-77
Project activity report: agricultural advisory services in Sudan’s main production states during the 2024/25 growing season
No full text11 page
Optimization of sparse phenotyping strategy in multi-environmental trials in maize
No full textThe phenotyping needs to be optimized and aims to achieve desired precision at low costs because selection decisions are mainly based on multi-environmental trials. Optimization of sparse phenotyping is possible in plant breeding by applying relationship measurements and genomic prediction. Our research utilized genomic data and relationship measurements between the training (full testing genotypes) and testing sets (sparse testing genotypes) to optimize the allocation of genotypes to subsets in sparse testing. Different sparse phenotyping designs were mimicked based on the percentage (%) of lines in the full set, the number of partially tested lines, the number of tested environments, and balanced and unbalanced methods for allocating the lines among the environments. The eight relationship measurements were utilized to calculate the relatedness between full and sparse set genotypes. The results demonstrate that balanced and allocating 50% of lines to the full set designs have shown a higher Pearson correlation in terms of accuracy measurements than assigning the 30% of lines to the full set and balanced sparse methods. By reducing untested environments per sparse set, results enhance the accuracy of measurements. The relationship measurements exhibit a low significant Pearson correlation ranging from 0.20 to 0.31 using the accuracy measurements in sparse phenotyping experiments. The positive Pearson correlation shows that the maximization of the accuracy measurements can be helpful to the optimization of the line allocation on sparse phenotyping designs
Accounting for the impact of genotype and environment on variation in leaf respiration of wheat in Mexico and Australia
No full textAn approach to improving radiation use efficiency (RUE) in wheat is to screen for variability in rates of leaf respiration in darkness (R dark). We used a high-throughput system to quantify variation in R dark among a diverse range of spring wheat genotypes (301 lines) grown in two countries (Mexico and Australia) and two seasons (2017 and 2018), and in doing so quantify the relative importance of genotype (G) and environment (E) in influencing variations in leaf R dark. Through careful design, residual (unexplained) variation represented <10% of the total observed. Up to a third of the variation in R dark (and related traits) was under genetic control. This suggests opportunities for breeders to use R dark as a novel selection tool. In addition, E accounted for more than half of the total variation in area-based rates of R dark. Here, the day of measurement was crucial, suggesting that day-to-day variations in the environment influence rates of R dark measured at a common temperature. Overall, this study provides new insights into the role G and E play in determining variation in rates of leaf R dark of one of the most important cereal crops, with implications for future improvements in carbon use efficiency and yield.1099–111