Soil carbon and nitrogen emissions under farmer managed conservation agriculture in Zimbabwe

Worldwide 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

Surface albedo and thermal radiation dynamics under conservation and conventional agriculture in subhumid Zimbabwe

While 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

Ensuring sustainable crop production when yield gaps are small: A data-driven integrated assessment for wheat farms in Northwest India

Northwest India achieved remarkable wheat productivity gains during the past decades. However, this has been accompanied by increasing input levels and intensive production practices, raising questions about the economic and environmental sustainability of current cropping systems. A multicriteria integrated assessment is required for wheat farms in the region to understand the scope for cleaner wheat production in the future. Production practices from irrigated wheat fields (n = 3928) were evaluated for multiple sustainability indicators, namely yield gap, nitrogen (N)-use efficiency, profitability, and greenhouse gas emissions. Stochastic frontier analysis was combined with simulated potential yield (Yp) data to identify the causes of wheat yield gaps in the region. N-use efficiency was estimated by calculating the partial factor productivity of N, profitability was computed based on reported input-output amounts and prices, and greenhouse gas emissions were quantified using the Mitigation Options Tool (MOT). These indicators were subjected to a multicriteria assessment using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) under different scenarios (i.e., different weights for different indicators). For each scenario, farmers’ fields were classified as most efficient, efficient, less efficient, and least efficient, and random forest was used to identify the most important management practices governing the field classification. Wheat yield gaps were small (25–30 % of Yp or 2.4 t ha−1) and mostly attributed to the technology yield gap (ca. 20 % of Yp or 1.5 t ha−1). Ranking and grouping the farmers’ fields in the scenario with equal weights for all indicators revealed that at least 25 % of the fields had very high greenhouse gas emissions (>1500 kg CO2-eq ha−1) at a productivity level of 80 % most efficient fields adopting zero tillage) to achieve an overall objective of higher yield, lower greenhouse gas emissions, more profit and higher N-use efficiency, whereas residue retention and tillage intensity would need to be prioritized for minimizing greenhouse gas emissions. For the most efficient fields the decrease in greenhouse gas emissions was always associated with a decline in yield level. The most important management practices governing the field classification included the crop establishment method used for the previous rice crop, the number of tillage operations, residue retention, and the N fertilizer rate for wheat. The study provides a data-driven approach to screen trade-offs between performance indicators and to identify the management practices that can deliver sustainable and cleaner crop production in the future

Molecular screening of septoria-resistant genes in historical Turkish bread wheat germplasm using the validated gene specific SSR markers

Septoria tritici blotch (STB), caused by Zymoseptoria tritici, poses a significant threat to global wheat production, particularly in T & uuml;rkiye. Resistance breeding is the most sustainable and effective disease control method. Molecular markers, especially simple sequence repeat (SSR) markers are extensively employed in wheat breeding to enhance the efficacy. The primary objective of this study was to identify Stb resistance genes among 143 historical registered Turkish bread wheat genotypes released as commercial cultivars between 1963 to 2014, using 16 closely linked SSR markers. The findings revealed substantial genetic variation among the screened cultivars, with the Stb3 gene being the most prevalent, identified in 89.51% of the samples. Other notable resistant genes included Stb13 (71.32%), Stb4 (43.33%), and Stb11 (41.25%). Cultivars Porsuk-2811, Porsuk-2853, and Porsuk-2868 exhibited the highest level of resistance to STB, with 10 resistance genes detected. Of the 143 cultivars screened, 10 were found to carry a total of nine Stb genes, while two cultivars were observed to possess only a single resistance gene. The study identified 23 wheat cultivars harboring 8-10 Stb resistance genes, which are highly recommended for future wheat breeding programs and gene pyramiding strategies to combat Z. tritici. This research provides critical insights for national breeding programs, supporting the development of resilient and high-yielding wheat varieties resistant to STB.89-10

A conceptual framework for the contextualization of crop model applications and outputs in participatory research

Contextualization of generic scientific knowledge to context-specific farmer knowledge is a necessary step in farmers’ innovation process, and it can be achieved using crop and farm models. This work explores the possibility to simulate a large number of scenarios based on farmers’ descriptions of their environment and practices in order to contextualize the discussion for each participating farmer. It presents a novel framework consisting of six actions divided in three phases, namely, phase I—reaching out to the farmers’ world: (i) project initialization; (ii) determination of the agronomical question anchored in farmers’ context; (iii) characterization of the environment, the management options, and the indicators to describe the system under consideration; phase II—within researchers’ world: (iv) crop model parametrization; (v) translation of model outputs into farmer-proposed indicators; and phase III—back to farmers’ world: (vi) exploration of contextualized management options with farmers. Two communication tools are created during the process, one containing the results of simulations to feed the discussions and a second one to create a record of it. The usefulness of the framework is exemplified with the exploration of soil fertility management with manure and compost applications for sorghum production in the smallholder context of Sudano-Sahelian Burkina Faso. The application of the framework with 15 farmers provided evidence of farmers’ and agronomists’ understanding of options to improve cropping system performance with better organic amendment management. This approach allowed farmers to identify and relate to the scenarios simulated, but highlighted interrogations on how to adapt the crop model outputs to particular situations. Though applied on issues related to tactical change at field level, the framework offers the opportunity to explore broader issues with farmers, such as farm reconfiguration

How has scientific literature addressed crop planning at farm level: A bibliometric-qualitative review

Crop planning (CP), being the core of farm management and decision-making, remains significant as the selection and allocation of appropriate crops determine the economics and sustainability of farming system. A systematic literature review was conducted to obtain a structural overview and consolidate the knowledge from CP literature, given the dearth of review articles in this domain. The methodology included systematic selection of literature in phases and mixed-method systematic review process consisting of bibliometric analysis and qualitative review. This enabled an understanding the main characteristics of CP literature and answer how CP has been addressed at farm level. 1516 publications were selected in first phase after which 652 were screened using bibliometric analysis software, VOSviewer and CiteSpace, in second phase to identify research hotspots and recent trends. Optimization, irrigation, sustainability, adaptation were certain hotspots, while a shift in research trend was observed from decision support, crop allocation and bioenergy to climate change, water resources and big data. Last phase focussed on qualitative review of 31 publications on farm. Three broad themes of articles emerged namely "farmer's decision-making", "soil-water-agroecology" and "merits of innovative technologies". The study proposed several recommendations for small farming systems which were largely ignored in literature. These include factorial design for crop combinations, choices in options, estimation of crop diversity index and relative time- dispersion in yields. The current review produced a macroscopic overview of accumulated knowledge on CP and provided future directions to harness the unexplored potential in this field

Macrofauna accelerates nutrient cycling through litterfall in cocoa agroforestry systems

This study aimed to better understand nitrogen (N), phosphorus (P), and potassium (K) cycling through litterfall in smallholder cocoa agroforestry systems and to assess if these nutrient flows can be measured using standard litterbags. Annual litter production, relative mass loss, and nutrient loss rates from cocoa leaf litter were evaluated in three farms in south-western Nigeria with and without macrofauna access. Litterfall was measured fortnightly close to the base of the cocoa tree and at the edge of the tree canopies from January 2020 to December 2021. Leaf litter decomposition rates were determined over 388 days in 2 mm mesh litterbags to exclude macrofauna and in frames open to the soil surface to allow macrofauna access. Concentrations of C, N, P, and K were measured in the remaining litter at 180, 244, 314, and 388 days after incubation. Annual estimates of litterfall (10.62 Mg DM ha−1) did not significantly differ between the traps close to and away from the cocoa tree trunk. Nutrient cycling from litter was estimated at approximately 101 kg N, 5 kg P, and 89 kg K ha−1 year−1. Relative litter decomposition rates (k) significantly differed between frames and litterbags. Macrofauna access significantly reduced the C:N ratio in the remaining litter and increased N and P loss from the litter layer by 28 and 69%, respectively. In conclusion, nutrient flows through litterfall are considerable, and N and P transfer rates to soil are likely underestimated in litterbag experiments that exclude macrofauna.427–44

Harnessing phosphocompost extracts to mitigate Meloidogyne javanica impacts on tomato

This study evaluated the chemical properties of phosphocompost extracts and their effectiveness in inducing tomato seedlings resistance to Meloidogyne javanica. Phosphocomposts: Sugar beet phosphocompost (PC-SB: CP2), green waste phosphocompost (PC-GW: CP3), and olive mill waste phosphocompost (PC-OMW: CP4), were utilized to produce compost water extracts at concentrations of 1:5, 1:10, 1:20, and 1:100 g:mL and then applied as soil drenches for tomato seedlings one-week post-inoculation. The CP2 extract applied at a 1:5 dilution led to marked improvements in growth parameters, with plant height increasing by over 52.2%, shoot fresh biomass rising by approximately 52.44%, and shoot dry biomass showing a gain of 62.21%. Root biomass also rose by 33%. Chlorophyll a increased with CP4 at 1:5 and 1:100 (41.05% and 37.32%), chlorophyll b increased with CP3 at 1:5 and 1:10 (22.34% and 7.59%), while carotenes showed no variation. Polyphenols rose by 86.45-91.01% with CP2 from 1:5 to 1:20, and flavonoids increased by 64.90% with CP4 at 1:10. CP2 diminished the ultimate M. javanica population and reproduction factor by 171.43%, while CP4 at 1:20 decreased egg masses by 151.94%. The root gall index showed no variation. The chemical composition of phosphocomposts revealed that the strategic incorporation of diverse organic improvers (10%) in phosphocomposts yielded distinct nutrient signatures, with sugar beet waste enhancing PO43- (12.91 mg/L) and secondary macronutrients, green waste optimizing NO3- (69.91 mg/L) and SO42- (62.70 mg/L) availability, and olive mill waste producing superior micronutrient concentrations alongside dominant Ca (24.21 mg/L), K (392.50 mg/L), and P (9.17 mg/L) levels. Overall, the results underscore the potential of phosphocompost extracts as a viable, low-cost, and eco-friendly alternative to synthetic nematicides, offering a sustainable and resilient approach to M. javanica control while enhancing tomato plant growth

Metabolic marker-assisted genomic prediction improves hybrid breeding

Hybrid breeding is widely acknowledged as the most effective method for increasing crop yield, particularly in maize and rice. However, a major challenge in hybrid breeding is the selection of desirable combinations from the vast pool of potential crosses. Genomic selection (GS) has emerged as a powerful tool to tackle this challenge, but its success in practical breeding depends on prediction accuracy. Several strategies have been explored to enhance prediction accuracy for complex traits, such as the incorporation of functional markers and multi-omics data. Metabolome-wide association studies (MWAS) help to identify metabolites that are closely linked to phenotypes, known as metabolic markers. However, the use of preselected metabolic markers from parental lines to predict hybrid performance has not yet been explored. In this study, we developed a novel approach called metabolic marker-assisted genomic prediction (MM_GP), which incorporates significant metabolites identified from MWAS into GS models to improve the accuracy of genomic hybrid prediction. In maize and rice hybrid populations, MM_GP outperformed genomic prediction (GP) for all traits, regardless of the method used (genomic best linear unbiased prediction or extreme gradient boosting). On average, MM_GP demonstrated 4.6% and 13.6% higher predictive abilities than GP for maize and rice, respectively. MM_GP could also match or even surpass the predictive ability of M_GP (integrated genomic-metabolomic prediction) for most traits. In maize, the integration of only six metabolic markers significantly associated with multiple traits resulted in 5.0% and 3.1% higher average predictive ability compared with GP and M_GP, respectively. With advances in high-throughput metabolomics technologies and prediction models, this approach holds great promise for revolutionizing genomic hybrid breeding by enhancing its accuracy and efficiency

A retrospective analysis of maize performance under low nitrogen stress conditions in sub-Saharan Africa

Introduction: Fertilizer use in sub-Saharan Africa (SSA) is the lowest in theworld and has stagnated. Consequently low nitrogen (N) stress is one of the principal constraints to maize yields in this region. Therefore improving nitrogen use efficiency of maize varieties will result in higher nitrogen recovery rates, leading to less leaching of nitrogen as well as loss through nitrification and ammonification. This study aimed to: 1) Investigate the relationship between grain yield under low N and optimal conditions; and 2) Establish the level of variability in low N tolerance among elite Eastern and Southern African (ESA) maize varieties. Methods: Fifty-eight paired trials, each consisting of 40 to 65 maize hybrids, were conducted under low N and optimal (i.e.,high N) conditions in five countries, in Eastern and Southern Africa during 2013-2015. Results and discussion: The level of yield reduction as a result of low N stress ranged from 8% to 91% across the 58 paired trails. Grain yield of hybrids ranged from1.69Mg ha-1 to 3.44 Mg ha-1 in the early maturity group and 1.71 Mg ha-1 to 3.35 Mg ha-1 in the intermediate to late maturity group, with heritability ranging from 0.25 to 0.53 and 0.29 to 0.76, in the respective two maturity groups. Under the low N stress. Pre-commercial hybrids that were bred for low N tolerance performed better than the old commercial hybrids and open pollinated varieties (OPVs). These results suggest that if more effort is devoted to selecting maize under low N conditions, significant yield gains can be realized with profound impact on maize productivity in SSA