Agronomy

The aim of this study was to assess the efficiency of two intercrop species, Canavalia ensiformis and Desmodium intortum, to reduce weed growth, herbicide use and damage by the stalk borer Chilo sacchariphagus in sugarcane cropping system in Reunion. We compared six inter-row management techniques: four treatments combined the two intercrops Canavalia ensiformis or Desmodium intortum sown either early (between 0 and 2.1 months after sugarcane emergence) or late (between 1.3 and 3.7 months after sugarcane emergence), a treatment where no weeding was carried out on the inter-row, and a treatment with chemical weeding of the inter-row (CcWp). In all treatments, the sugarcane row was weeded chemically and manually. The six treatments were compared in a multilocal randomized block design with three localities, during one or two crop cycles depending on the locality. Desmodium intortum produced poor ground coverage in half of the trial x crop cycles and was therefore found unsuitable for use as an intercrop of sugarcane in our conditions. On the opposite, Canavalia ensiformis quickly covered the inter-rows, regardless of the sowing date. The Canavalia ensiformis intercrops allowed a reduction of herbicide consumption by 63% when sown early and by 28% when sown late, compared to the CcWp control treatment. Both Canavalia ensiformis intercrops caused a reduction of weed coverage on the inter-row similar or better than the chemical control. However, the early sown Canavalia ensiformis intercrop caused a 18.6 t.ha-1 yield loss compared the chemical control. No yield loss was detected with the late sown intercrop. A significant reduction of stalk damage by a 0.8-fold factor was observed in the early sown Canavalia ensiformis treatment.

This study investigated the mutagenic effects of ethyl methane sulfonate (EMS) on the M{square} generation in cowpea (Vigna unguiculata (L.) Walp.) cultivar Wang Kae. A total of 275 M{square} seeds were treated with EMS concentrations of 20 mM, 40 mM, and 80 mM (75 seeds per treatment) by soaking for six hours, while 50 untreated seeds served as the control (0 mM). Phenological, yield-related and yield traits were recorded, and data were analysed using Jamovi 2.7.15 and JASP 0.95.4.0 through one-way ANOVA with post hoc contrast, principal component biplot, and cluster analyses. No optimal mutagenic concentration (LD50) was identified. Seed germination and seedling survival rates increased with increasing EMS concentration, ranging from 70.00% and 62.00% in the control (0 mM) to 89.33% and 74.67% at 80 mM, following the trend 0 mM < 20 mM < 40 mM < 80 mM. Significant differences (P < 0.05) were observed among treatments for all phenological traits, pod length, locule number, seed traits, and yield per plant. Yield was significantly higher (P = 0.047) at 20 mM (61.19 {+/-} 3.34 g) compared to the control. Contrast analysis identified genotypes B33 and D56 as the most productive mutants, with yields of 125.44 g and 111.85 g, respectively. Principal component analysis extracted eighteen components, with the first four cumulatively explaining 50.60% of total variation. Biplot analysis of PC1 and PC2 captured all phenological traits, key seed traits, and yield attributes, highlighting the superior performance of B33 and D56. Cluster analysis partitioned the 190 genotypes into six groups, with B33 and D56 constituting distinct clusters. EMS mutagenesis effectively induced heritable phenotypic variation, with putative superior genotypes identified for advancement to M{square} and evaluation in replicated multi-environment trials toward the development of farmer- and consumer-preferred cowpea varieties.

Optimization of Multi environment trials (MET) trials require an efficient testing network consisting of ideal sites which should be highly discriminative and representative of their mega environments. Also concurrently managing sites that are similar in terms of information there give about genotype performance adds no extra utility to the breeding program. The objective of this study was to appraise the testing network of the cowpea breeding program of the International Institute of tropical Agriculture (IITA) based in Nigeria on the basis of their representativeness and discriminatory ability for grain yield, investigate the similarities among them and assess the variance components. 6 set sets of Advance yield trial with unique entries were analysed using Mixed models, best linear unbiased estimate (BLUPs) and BLUP based GGE biplot. Results showed significant means squares Genotype x Location interaction effect for all 6 sets; this justifies the to study GEI in cowpea, partitioning variance showed that Environment main effect accounted for the largest proportion of the total phenotypic variance, with a range of 58.1 (Adv 4) to 73.9% (Adv 3), Environment was followed by Genotype x Location interaction which explained between 16.1% (Adv 5) to 22.6% (Adv 4). Genotype main effect accounted for the least with a range of 9.5% (Adv 6) to 19.3% (Adv 4). Broad sense-heritability for GY was high in Advance 1-5 (ranging from 0.62-0.70) and medium in advance 6 (0.53). Shika June, Shika August and Minjibir consistently showed high discriminativeness with Minjibir been the most discriminating environment for this study. Ibadan September and Ibadan May on the other hand consistently showed poor discriminating ability while BUK was inconsistent having good discriminative ability in Sets 1 and 3 while it was poor in sets 2, 5 and 6. In terms of representativeness, no environment consistently had desirable results however, Shika August and Shika June were most representative while Minjibir was least representative. On the basis of discriminativeness and representativeness, the six environments were ranked in other of desirability as Shika August> Shika June> Minjibir > Ibadan September > BUK farm > Ibadan May. In terms of similarity among environments, both the BLUP-Based GGE biplot and genotypic correlations indicated that Ibadan May and September were consistently grouped together and highly correlated; indicating that they are similar, while, Shika August and Shika June were found to be unique. Ibadan May was therefore adjudged a redundant location and could be dropped and replaced without any loss of accuracy because it was neither discriminative nor representative in all biplots draw and it consistently fell in the same Mega environment with Ibadan September. We concluded that there is a need to sample more testing sites and validate their fitness for multi environment yield trials using methods applied in this study.

Soil salinity and sodicity are among the major challenges threatening agricultural productivity in the Central Rift Valley of Ethiopia. A column experiment was conducted in laboratory on saline-sodic soils of Metehara Sugar Estate to evaluate the effectiveness of phosphogypsum and leaching in reclaiming these soils. The treatments comprised of five rates of phosphogypsum equivalent to 50, 75, 100, 150, and 200% gypsum requirement, 100% gypsum requirement of natural gypsum, and an absolute control with no amendments applied, and five volumes of leaching water. The treatments were arranged in Complete Randomized Design with three replications. The leaching water was applied to the columns in an intermittent ponding mode. Leachates and soil samples collected from the columns after termination of the leaching process were analyzed for selected soil properties. Results showed that applying phosphogypsum at a rate of 100% gypsum requirement or higher (which is equivalent to [&ge;] 13 tons/ha) along with 3-4 pore volume of leaching water was found to be the most effective combination to reduce salinity and sodicity to levels that are suitable for most crops (ECe <4 dS/m and ESP < 10%,). The efficiency of phosphogypsum equivalent to 200% gypsum requirement was 81% and 75% in soluble salt removal and Na reduction, respectively. Results of the study suggest that phosphogypsum is a promising reclamation material for saline-sodic soils. However, a field experiment has to be conducted to evaluate the effectiveness of these amendments under natural conditions and come-up with implementable rate recommendations.

Spinach (Spinacia oleraceae) is a principal vegetable crop commercially grown in Controlled Environment Agriculture (CEA). Recent research suggests that root morphological and architectural differences among crop species influence yield, resource use efficiency, and environmental stress tolerance. These root traits may be exploited to increase yield, promote efficient nutrient use, and mitigate environmental stressors. This study measured differences between various spinach cultivars in CEA systems to reveal morphological and anatomical variation. We grew three spinach cultivars with different reported growing rates ( Income, Darkside, and El-Majestic) under NFT hydroponic and substrate-based systems in a controlled greenhouse environment over 45 days with destructive harvests at days 15, 30, and 45. Supplemental light (250 {micro}mol/m2/s) with 12-hour photoperiod and periodic fertigation was used. Harvests included the collection of leaf and root biomass, and scanning of root systems in WinRhizo software, measuring ten variables. On day 45, root cross-sections from orders 1-5 were embedded in JB-4 resin, sectioned, stained, and analyzed for diameter, vasculature, and rhizodermis characteristics. Results indicate that in spinach, differences in root system morphology are linked to cultivation systems over cultivar identity. Vascular and root anatomical alterations are minor compared to morphological differences in response to the cultivation system. Hydroponic-style growth systems are associated with the proliferation of fine-root ideotypes compared with substrate-based conditions. Such findings affirm previous studies, which suggest plastic root morphology in response to growth systems, and may be used to help create more resilient, resource-efficient cultivars. HighlightsO_LIIn spinach, root system morphology differences are linked to cultivation systems. C_LIO_LIRoot vascular and anatomical alterations are minor in response to cultivation system. C_LIO_LIHydroponic growth systems are linked to fine-root ideotype proliferation in spinach. C_LIO_LIFine-root ideotype proliferation may be a breeding target for CEA spinach. C_LI

High yield and stability of performance across environments are required for success in agricultural systems. Selection of high yielding and stable genotypes is always hindered by Genotype by environment interaction (GEI). Hence plant breeders have proposed diverse methods to help increase their efficiency of selection in multi environment yield trials. The aim of this experiment was to identify and select high yielding and stable cassava genotypes using to GGE biplot and Yield selection index (YSi) techniques and to determine the efficiency of the techniques. 24 hybrids and 2 checks were tested across five locations in Nigeria using the randomized complete block design with 4 replications. ANOVA provided clear evidence of GEI (P<0.001) for dry root yield. 23 of the hybrids showed 10% to 83% economic improvement over the best check variety. GGE biplot identified genotypes TMS-IBA090574, TMS-IBA090521, TMS-IBA090590 and TMS-IBA090576 as high yielding and stable, while YSi selected 14 genotypes TMS-IBA090454, TMS-IBA090504, TMS-IBA090506, TMS-IBA090509, TMS-IBA090521, TMS-IBA090523, TMS-IBA090536, TMS-IBA090564, TMS-IBA090574, TMS-IBA090576, TMS-IBA090581, TMS-IBA090590, TMS-IBA090597 and TMS-IBA090609. Six of these genotypes (TMS-IBA090523, TMS-IBA090536, TMS-IBA090574, TMS-IBA090581, TMS-IBA090506, TMS-IBA090521) were marked as unstable by the Shuklas stability variance which indicates a weakness of YSi. The two techniques identified TMS-IBA090590 and TMS-IBA090576 as high yielding and stable genotypes. These two clones performed above average in at least four of the five locations used for evaluation. The genotypes jointly selected by both methods may therefore be released to increase the productivity of the crop in Nigeria and comparable agro ecology.

Organic farming supports environmental sustainability by saving water, safeguarding ecosystems, and providing economic opportunities through organic crops. It promotes food security and long-term development in the arid regions. However, its adoption in Jordan remains limited, primarily due to insufficient governmental support policies and measures. This study aims to identify the fundamental barriers to the adoption of organic agriculture in Jordans arid regions, evaluate farmers preparedness for organic practices, explore opportunities for organic farming, and propose recommendations to enhance its adoption. The study utilized a longitudinal approach, conducted in two phases over two decades. The first phase (April-September 2004) involved semi-structured interviews with 46 farmers and five focus groups. The second phase (July-September 2024) revisited seven experienced farmers from the initial cohort, using a phenomenological research approach a widely used approach. The results of phase 1 findings showed that the main barriers were technical, economic, marketing, legislation, institutional and extension and services while socio-cultural was not. The results of phase 2 highlighted persistence of the barriers identified in phase 1, alongside unresolved institutional difficulties, including certification processes, regulatory gaps, and limited market access. The study concluded that implementing streamlined certification procedures, government-supported subsidies, education programs, and policy modifications to promote sustainable adoption of organic farming and farmer engagement in Jorden. The limitation includes a small sample size, the two-decade gap between phases, and a focus on arid regions only. Further, it excludes other stakeholders perspectives, underexplores socio-cultural factors and provides limited analysis of certification, market access, and comparable contexts.

Environmental factors affect crop growth and development thus their consideration across sites and years become essential for genotypic evaluation. Genomic selection (GS) has been broadly implemented to accelerate breeding cycles by skipping field evaluations thus allowing early identification of outperforming genotypes. In this study, 7,740 phenotypic records corresponding to 516 Miscanthus sacchariflorus genotypes evaluated in five locations across three years were considered for analysis. Additionally, environmental data on six weather covariates was implemented to characterize similarities between locations. Different sets of locations of variable sizes were used for model calibration based on two cross-validations (CV00 and CV0) schemes leaving out one location at a time. Predictive ability across locations of the best model varied between 0.45 and 0.90 for both schemes. These results were compared to associate predictive ability in function of weather patterns between training and testing sets to allow models calibration optimization. We found it is feasible to optimize resource allocation by considering environmentally correlated sets. In most cases, the information from only one and, at most, two locations were enough to deliver better results than using all four locations, reducing training sets by up to 75%. The results obtained shed light on helping breeders make informed decisions considering weather data when designing evaluations.

The self-incompatibility, perennial growth habit, large tree size, and long juvenility present challenges in applying traditional breeding approaches in almond (Prunus dulcis Mill. D. A. Webb). Moreover, nut and kernel traits in almond are mainly controlled by a large number of small-effect quantitative trait loci (QTLs) and improving complex traits through conventional breeding approaches is slow and often inefficient. Genome-wide selection represents a promising strategy to enhance the efficiency of cultivar identification and selection of superior parents in almond breeding programs by estimating the breeding values (BVs) at early maturity. The main aim of this study was to implement genomic (GBLUP) and pedigree-based (ABLUP) prediction approaches to estimate BVs to identify the superior parental candidates for improving nut and kernel traits in almond. Here, we estimated BVs for nine traits that are commonly used in the primary evaluation stage of the almond breeding using genomic data from 61 parents and phenotypic data of 15,281 progeny derived from 205 unique families. Breeding values obtained from both approaches showed a strong correlation (r [&ge;] 0.94) for all traits except shell seal (r = 0.87). The population structure analysis conducted using high-quality 90K single nucleotide polymorphisms (SNPs) indicated clear separation of the Californian, European and some old Australian almond cultivars, with considerable admixture across some cultivars. Following further validation, both prediction approaches could be useful in early identification of superior candidates. The slightly higher breeding values obtained using the GBLUP compared to the ABLUP approach suggest that accounting for within-family variations and realised genomic relationships can enhance prediction accuracy, reliability, and overall genomic prediction performance in almond.

Genomic selection holds the potential to serve as a strategic tool to enhance the genetic gain of complex traits in Miscanthus breeding programs. The development of improved cultivars requires their assessment for various traits across diverse environments to ensure suitable overall performance. Hence, the multi-trait multi-environment (MTME) genomic prediction (GP) models offer an opportunity to improve selection accuracy. This study aims to evaluate the potential of five GP models: (1) three MTME models including genotype-by-trait-by-environment interaction (GxExT) and (2) two single-trait multi-environment (STME) models (with and without GxE interaction). A Miscanthus sacchariflorus population comprising 336 genotypes evaluated in three environments and scored for four traits (biomass yield YDY, total culm number TCM, average internode length AIL, and culm node number CNN) was analyzed. The predictive ability of the models was evaluated considering three cross-validation schemes resembling realistic scenarios (CV1: predicting new genotypes, CVP: predicting missing traits in a given environment, and CV2: predicting partially observed genotypes). On average, in all cross-validation schemes compared to the STME the predictive ability of the MTME models was 10% to 70% higher for TCM and AIL. On the other hand, for YDY and CNN, both STME models performed similarly or slightly better (between 5 to 64%) than the MTME models in most environments. While the MTME models were not successful for all traits when compared to their STME counterparts, MTME models improved the prediction of the performance of genotypes that were untested across environments or lacked trait information in a specific environment. Overall, our study suggests that MTME GP models can be implemented in Miscanthus breeding programs to improve the predictive ability of the complex traits, shorten breeding cycles, and accelerate selection decisions.

Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse biobased products. Increasing biomass yield will increase profitability and environmental benefits, so is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; GxE interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for GxE interaction presented the highest PA and the lowest MSE for CNN (PA: [~]0.77, MSE: [~]0.5) and YDY (PA: [~]0.70, MSE: [~]1.3) while for TCM and AIL these ranged from [~]0.28 to 0.41 and [~]1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus.

Net radiation (Rn) can be estimated using models that apply the Brunt equation for the incoming longwave radiation and air temperature (Tair) for the outgoing longwave radiation under reference conditions. This study aimed to estimate Rn using two previously regionally calibrated Brunt model, thereby eliminating the need site-specific calibration, and to assess whether Tair can be used as a substitute for canopy temperature (Tc) under well-watered crop conditions. Measurements were conducted in sesame and cotton fields during the first year and in a cotton field during the second year. Canopy temperature was measured during the second year, and the calculations were performed at hourly and daily time scales. Regardless of the method used to estimate sky emissivity or whether Tc or Tair was used, errors were greater at hourly time scale. The overall RMSE, MAE, Bias and KGE values at the daily time scales were 11.88, 9.13, 2.53, and 0.91, in the first year, and 13.45, 10.56, 0.10 and 0.74, in the second year, respectively. When using both regionally calibrated Brunt model, Rn simulation performance was superior to that of the Allen/FAO method. The comparison between Rn estimated using Tair and Tc, indicated statistical differences. Nevertheless, linear regression and error metrics showed that these differences were modest, especially at daily time scale. Thus, for practical purposes both regionally calibrated Brunt equations can be used to calculate clear-sky emissivity and improve Rn estimations, and Tair can be used as a substitute for Tc at the daily time scale under well-watered conditions.

Small-scale vegetable farms are increasingly important to local food systems, but the soils on these farms are not well understood, particularly in high tunnel production environments. Therefore, this study aimed to 1. Compare soil nutrients and soil health metrics in high tunnels and nearby open fields. 2. Document soil nutrient accumulation on diversified vegetable farms and assess loss potential. 3. Explore the impacts of specific management practices (input use, cover crops, tillage, and soil testing) and farm demographics on a variety of soil health and soil nutrient metrics. Just under half of the high tunnels in this study had soluble salt accumulation, which was associated with higher soil nitrate concentrations. The pH of many high tunnel soils was above the optimal range for crop production, which was correlated with irrigation water alkalinity. Some high tunnel soils had rapid water infiltration rates, with implications for irrigation water management. Both high tunnel and open field soil were rich in nutrients compared with other Minnesota farms. Preliminary assessments suggested risks to surface and groundwater from nutrient runoff and leaching. While farmer experience and more years in vegetable production were negatively associated with soil health metrics, management practices including reduced tillage, organic management, and application of plant-based compost were positively associated with soil health. Cation exchange capacity and permanganate oxidizable carbon did not provide significantly more insight than simply measuring organic matter. Arbuscular mycorrhizal fungal spore counts were inconclusive, but aggregate stability and bulk density were responsive to farmer reported soil management activities. Core ideasO_LIHigh tunnel soil tends to be rich in nutrients and organic matter. They also accumulate soluble salts, likely from excess inputs C_LIO_LIIrrigation water routinely tested high in pH and alkalinity. These factors may explain high soil pH in high tunnels. C_LIO_LISmall-scale vegetable farms often have high concentrations of soil nutrients in both high tunnels and open fields, with potential to cause environmental contamination through leaching and runoff C_LIO_LIVegetable production may be inherently hard on soil health, but conservation practices including reduced tillage, organic management, and use of plant-based composts can improve soil health in these production systems C_LI

Giant Miscanthus giganteus (Mxg) is one of the most promising perennial crops to generate biomass feedstock for bioenergy and biobased products. It is derived from the natural inter-species hybridization of Miscanthus sacchariflorus (Msa) and Miscanthus sinensis (Msi) species, thus population improvement within these species is crucial. Genomic selection (GS) is an attractive option to accelerate breeding of perennial grasses, such as Miscanthus, which requires up to three years of evaluation to produce reliable phenotypic data. Hence, genotypes are observed in multiple years and locations causing inconsistent response patterns from one year to the next, between location, and/or location-by-year combinations. These inconsistencies are known as the genotype-by-environment interaction effect (GxE). Although GS has been successfully implemented in multiple annual crops where straightforward cross-validation schemes exist to assess the levels of predictive ability that can be reached, for perennial crops new cross-validation schemes will help avoid data contamination. Here, we propose a series of cross-validation schemes to evaluate model performance for perennial crops. We perform a case study by analyzing one panel of each species (516 genotypes of Msa, 280 genotypes of Msi) scored for biomass yield at different locations around the world over several years. The results of the different cross-validation schemes provide insights about the usefulness of GS to accelerate the breeding process of Miscanthus species. In addition, leveraging the GxE effects of different types significantly increases predictive ability (up to 10% in Msa and 30% for Msi) compared to the conventional approaches based on main effects only.

Blackleg disease in potato, caused by soft rot Pectobacteriaceae, is a substantial cause of loss in seed potato production. Recent research has attempted to identify bacteria with antagonistic activity against several diseases, among which blackleg. However, most biocontrol agents have been tested only in-vitro or in the greenhouse. In this study, we tested the effect of bacterial biocontrol agents in a four-year field experiment against blackleg caused by Pectobacterium brasiliense and Dickeya solani. Effects of the treatments on disease incidence was highly variable between years and also differed between cultivars, soil type and even replicates. Disease incidence was on average higher in sandy soil compared to clay soil and higher in the cultivar Kondor than Mozart. For a subset of the bacterial isolates genome mining could detect the presence of genes involved in the production of antibiotics and siderophores, but this was not correlated with disease incidence in the field. Moreover, most isolates were able to survive in storage on tubers from inoculation until planting. Thus, we conclude that while the used isolates showed the potential for antagonistic activity and were present on tubers when planting, no antagonist treatment could consistently decrease disease incidence. Inoculation of the isolates on the tuber surface might have been insufficient for plant colonization.

Plant architecture, the spatial configuration of stems, branches, leaves, and inflorescences underpins essential physiological functions such as light capture, assimilate partitioning, flowering, and ultimately, yield. In cassava (Manihot esculenta), architectural traits such us plant height, branching level, and plant shape are agronomically important yet remain underexploited in breeding. Here, a large-scale analysis was conducted using phenotypic and genomic data from more than 14,000 cassava accessions evaluated across 34 field locations in Nigeria between 2010 and 2021, encompassing the national breeding programs of the National Root Crops Research Institute and the International Institute of Tropical Agriculture. The study aimed to dissect the genetic architecture, environmental stability, and breeding relevance of four key traits: plant full height, height to first branching, the branching level number (BranchlevelNum) and plant shape. Phenotypic analyses across breeding stages revealed consistent variation in plant height, branching height, and branching intensity, reflecting the cumulative effects of selection and evaluation across environments. Broad-sense heritability estimates ranged from 0.41 to 0.72, with BranchlevelNum and Cylindrical shape exhibiting strong genetic control and weak correlations with yield components, indicating their suitability for independent improvement. Genome-wide association analyses identified significant loci associated with BranchlevelNum, including a major region on chromosome 2 and an additional locus on chromosome 13, collectively explaining approximately 11% of the phenotypic variance. Candidate genes within these regions included regulators of meristem activity and hormone-related pathways, supporting a developmental basis for branching variation. Genomic prediction accuracy for BranchlevelNum reached 0.44, comparable to values reported for key agronomic traits in cassava. These results demonstrate that branching-related architectural traits are genetically tractable, largely independent of yield, and amenable to genomic selection. The findings support the integration of BranchlevelNum and plant shape into ideotype-driven breeding frameworks aimed at improving flowering efficiency, canopy structure, and field performance in cassava. Author SummaryCassava is a major food crop, and its plant shape plays an important role in how easily it can be grown, harvested, and improved through breeding. Traits such as plant height, branching, and canopy form affect flowering, seed production, and field management, yet they have received much less attention than yield or disease resistance. In this study, we examined plant architecture using field and genetic data from more than 14,000 cassava plants grown across Nigeria over twelve years. We focused on key traits describing plant height, branching level, and overall plant shape. We found that branching level is strongly controlled by genetics, remains stable across environments, and can be predicted accurately using genomic data. We also identified specific regions of the cassava genome linked to branching behavior. Our findings show that plant architecture can be improved using modern breeding tools without compromising yield. Incorporating branching traits into breeding programs can help develop cassava varieties that flower more reliably and perform better in farmers fields.

Enset (Ensete ventricosum), a multipurpose crop domesticated exclusively in Ethiopia, serves as a staple food for millions of smallholder farmers. It is primarily cultivated as a monocrop in homegardens, leaving it vulnerable to climate change risks. One potential nature-based solution involves agroforestry systems; however, ensets response to canopy cover remains unclear. This study examined how scattered trees in enset farms affected microclimate and enset morpho-physiology in South Ethiopia. Trees significantly modified the microclimate conditions in enset homegardens. The average daily reductions in air, soil surface, and soil temperatures ranged from -0.5 to -1.9 {degrees}C, -0.4 to -2.1 {degrees}C, and +0.4 to -1.0 {degrees}C, respectively. The minimum soil moisture offset ranged from +0.8% to +5.7%. Although the tree identity effect on enset growth was negligible, planting position relative to the overstory trees significantly influenced enset responses. Most morphophysiological traits were higher under tree canopies, with progressively lower values at the edge and outside the tree canopy. In contrast, leaf dry matter content exhibited an inverse trend, aligning with the leaf economics spectrum. These results demonstrate ensets adaptability to canopy shade, suggesting potential for agroforestry expansion. Cultivar-specific shade tolerance and ideal shade levels to maintain enset productivity should be investigated further.

Quinoa (Chenopodium quinoa Willd.) is a genetically diverse Andean crop valued for its nutrition and adaptability to varied agroclimatic conditions with potential for cultivation in European and Mediterranean, particularly on marginal lands. Low temperatures during early sowing can impair germination, while delayed sowing increases the risk of poor maturation due to unfavorable autumn weather. To assess the adaptation of quinoa to cold stress, we evaluated germination and phenotypic variation in 60 accessions from highland and coastal ecotypes across three sowing dates in South-Western Germany: late winter (S1), early spring (S2), and spring (S3). Cold stress in S1 delayed seedling-emergence and reduced emergence percentages, yet these plants produced the highest average seed yield per plot (64 g) compared to S2 (46 g) and S3 (35 g). Highland accessions showed earlier seedling-emergence and with higher emergence percentages, while coastal types matured earlier and gave higher yields across sowing dates. A complementary laboratory experiment assessed germination under cold (4.4 {degrees}C) and control (18.3 {degrees}C) conditions, using both manual scoring and image analysis via a Mask R Convolutional Neural Network, to track seedling growth. This confirmed the beneficial germination performance of highland accessions under cold stress, with strong agreement between manual and automated scoring. Our findings suggest that quinoa demonstrates resilience to cold stress with highland quinoa exhibiting superior germination traits, and early sowing, despite reduced emergence, can lead to higher yields. We conclude that combining favorable traits such as faster maturity and higher yield of coastal ecotypes with superior germination traits of highland accessions is a promising avenue for breeding improved quinoa varieties for cold climatic regions.

CONTEXTRapeseed is a globally significant oil crop, exhibiting highly plastic responses among seed yield components (seed number and weight). However, there remains a notable gap in knowing the distribution of quality traits among seed size categories and understanding how seed size and source-sink (S-S) ratio influence comprehensive seed quality traits. OBJECTIVEThis study investigated the effects of seed size and S-S ratio reduction on the quality traits of winter and spring rapeseed genotypes. METHODSThe experiments were carried out at field conditions in Valdivia, Chile, where seed yield, yield components, oil, protein, and element concentrations (P, K, S, Ca, Mg, B, Cu, Fe, Mn, Zn, and Na) were evaluated across five seed size categories; very small (< 1.4 mm), small (1.4-1.7 mm), medium (1.7-2.0 mm), large (2.0-2.36 mm), and very large (> 2.36 mm). Treatments included a control and a reduced S-S ratio (75% shading), which significantly increased seed weight (P < 0.05). RESULTSBoth genotype and seed size affected (P< 0.050) the quality traits. Larger seeds exhibited higher Mg and B concentrations, as well as lower K, Ca, Fe and Na. Shading affected seed size distribution, favouring a higher proportion of large seeds. Under the shading treatment, the small seed category reached 5% lower oil concentration, while protein seed concentration increases 6% in both genotypes. Principal component analysis highlighted the complex interaction between yield, yield components, and quality traits, since there was no clear separation between different seed size categories and S-S ratio treatments. CONCLUSIONThese results provide insights into the plasticity of rapeseed quality traits, highlighting their collective impact on nutrient profiles. SIGNIFICANCEThis information is helpful for optimising cultivation practices and informing breeding programmes aimed at improving seed quality, particularly in high-yielding environments susceptible to environmental stresses. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=90 SRC="FIGDIR/small/707178v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@19d16eforg.highwire.dtl.DTLVardef@4cc16forg.highwire.dtl.DTLVardef@12f741borg.highwire.dtl.DTLVardef@6fa37a_HPS_FORMAT_FIGEXP M_FIG C_FIG

Wild potatoes are long-evolving relatives of the cultivated potatoes we have today. These wild Solanum species harbor traits that can be exploited to develop more nutritive and resilient potato varieties, providing the genetic basis for resistance to abiotic and biotic stresses such as drought, low temperatures, diseases and pests. Wild potato species are widely used as valuable genetic resources in breeding programs, including efforts aimed at improving resistance to bacterial wilt caused by Ralstonia solanacearum. Among the wild species native to Uruguay, Solanum malmeanum has emerged as a particularly valuable source of resistance. The aim of this work was to investigate weather differences in bacterial wilt resistance among S. malmeanum accessions are associated with structural and compositional changes of rhizosphere bacterial communities. Two S. malmeanum accessions were compared, one susceptible (RN9P2) and the other resistant (A11P1) to bacterial wilt. The impact of plant resistance and pathogen colonization on the structure of rhizosphere bacterial communities were evaluated using high throughput 16S rRNA gene amplicon-sequencing. Significant differences were observed between accessions and pronounced shifts in rhizosphere bacterial communities were detected in response to pathogen inoculation. Cryseobacterium, Sphingobacterium, Komagataeibacter, Gluconobacter, Lactobacillus and Dyella were differential genera and enriched in the rhizosphere of the resistant accession. Several of these genera have been previously associated with disease suppression. Overall, these results suggest that the rhizosphere bacterial community associated with resistant S. malmeanum accessions may contribute to protection against R. solanacearum infection.