Context. Production of sugarcane (Saccharum officinarum) in Brazil holds a significant global position. However, it faces challenges in yield optimization due to unfavorable edaphoclimatic conditions and technological limitations.

Aim. This study aimed to correlate the spatio-temporal variability of sugarcane yield with edaphoclimatic conditions in the central region of Brazil.

Methods. This study included 11 sugarcane producing municipalities in central region of Brazil. It utilized 47 years of historical data on yield, climate, and soil. To isolate the climatic effects on yield (Yr), technological trends (Yr_NT) were removed via simple linear regression adjustment, followed by cluster analysis.

Key results. Four groups of Yr and Yr_NT were identified. Group 1 exhibited the highest average yield (77 Mg ha⁻¹), while Group 4 had the lowest (47 Mg ha⁻¹), with a yield gap of approximately 10.2 Mg ha⁻¹. Municipalities with the highest Yr averages were in Clusters 3 and 4 for climate, and Clusters 2 and 3 for soil.

Conclusions. The higher occurrence of anomalies lower than 1.0 σ for climate Group 3 of Yr_NT indicates that unfavorable climatic conditions combined with inadequate production technologies can lead to significant yield losses (26% of years). Evaluating sugarcane yield by considering crop cycles and edaphoclimatic factors before technological trends can provide a more accurate insight into yield variability.

Implications. Understanding the relationship between edaphoclimatic factors and sugarcane yield variation can guide targeted interventions, aiding in the development of strategies to mitigate losses in sugarcane farming.

Soybean (Glycine max) is one of the most prominent legume crops, primarily being cultivated as a substitute for high-protein meat and a source of vegetable oil. Soybean has always been in demand worldwide due to its nutritional and economic value. Soybean and similar higher market-value products are used either directly or as a component in various soy-based items. Conventional breeding techniques have increased soybean yields for the past few years but are not able to meet the demands of the world’s rapidly growing population. Therefore, new genomic techniques are required to overcome those challenges. The role of novel molecular breeding techniques such as speed breeding, modifications of genome editing, genome-wide association studies, genomic selection, ‘breeding by design’, and RNA-directed DNA methylation are summarised in this review highlighting their future potential in soybean improvement. These techniques have opened up opportunities to introduce greater genetic diversity into the soybean germplasm. Different soybean yield, quality, and other agricultural traits including abiotic and biotic stresses have been improved using these techniques and research is underway to revolutionize the soybean genomic field.

Context. Automatic weed detection and control is crucial in precision agriculture, especially in cereal fields where overlapping crops and narrow row spacing present significant challenges. This research prioritized small weed detection and its performance in dense images by using innovative techniques.

Aims. This study investigated two recent convolutional neural networks (CNNs) with different architectures and detection models for weed detection in cereal fields. The feature pyramid network (FPN) technique was applied to improve performance. To tackle challenges such as high weed density and occlusion, a method of dividing images into smaller parts with pixel area thresholds was implemented, achieving an approximately 22% increase in average precision (AP).

Methods. The dataset includes red–green–blue (RGB) images of cereal fields captured in Germany (2018–2019) at varying growth stages. Images were annotated using ‘LabelImg’, assigning weed labels. Models were evaluated by precision, recall, prediction time, and detection rate.

Key results. The evaluation results showed that the FasterRCNN-ResNet50 with FPN had the best performance in terms of detection numbers. In the tests, the model successfully detected 508 of 535 annotated weeds in 36 images, achieving a detection rate of 94.95%, with a 95% confidence interval of [92.76%, 96.51%]. Additionally, a method was proposed to boost average precision and recall in high-density weed images, enhancing detection operations.

Conclusions. The results of this research showed that the presented algorithms and methods have a high ability to solve above-mentioned challenges.

Implications. This research evaluated deep learning models, and recommends the best and stresses reliable weed identification at all growth stages.

Context. Enteric methane (CH₄) emission from livestock accounts for 71% of greenhouse gas emissions from Australian agriculture.

Aims. To evaluate a range of pasture mixtures by using species with anti-methanogenic properties for their compatibility, productivity, feed quality and CH₄ emission reduction potential.

Methods. Two field experiments were conducted at Wagga Wagga and Cowra, New South Wales, Australia, from 2022 to 2024. In total, 33 pasture mixtures were evaluated over 3 years. Herbage was taken in spring from each pasture mix to analyse mineral composition, feed quality, plant secondary compounds and CH₄ yield from in vitro fermentation.

Key results. Methane yield was negatively correlated with saponins and condensed tannins. Perennial herbs, particularly plantain, had higher concentrations of condensed tannins and saponins than perennial grasses, at both sites. Overall, balansa clover was the species with the highest concentration of condensed tannins and biserrula had the highest concentration of saponins. However, plantain and biserrula lacked persistence at both sites. Chicory-based pastures were highly productive with high feed quality.

Conclusions. Pasture species higher in saponins and condensed tannins have great potential to reduce enteric CH₄ emissions. Pasture mixtures containing plantain and/or biserrula exhibited potential to reduce CH₄ emissions, but poor persistence may limit their adoption in some environments.

Implications. The study provided evidence that a number of highly productive pasture mixtures have potential to reduce CH₄ emission intensity and can be deployed within extensive livestock grazing systems, allowing producers opportunity to decrease their greenhouse gas liability in emission reduction schemes.

Context. Many wheat producers are increasing the biomass of cereal stubble retained after harvest through the adoption of stripper front harvesters, which result in taller standing stubble.

Aims. We investigated whether taller stubble affects the survival and dispersal of Fusarium pseudograminearum (Fp), the causative agent of Fusarium crown rot (FCR).

Methods. Field experiments at two sites in northern New South Wales were run for 3 years to investigate whether taller cereal stubble in Year 1 facilitated additional Fp colonisation, and subsequent effects on dispersal of Fp inoculum from chickpea harvest in Year 2 and FCR infection and expression in cereal crops in Year 3. Culturing and quantitative polymerase chain reaction (qPCR) methods assessed Fp colonisation and future disease risks.

Key results. In taller cereal stubble, Fp colonised an additional 91–92% of the stubble length in the 6 months post-harvest and persisted at higher levels for at least 1 year than did the shorter cereal stubble. Cutting cereal stubble short (in Year 1) therefore successfully restricted further colonisation by Fp. Significant displacement of Fp in the crown 6 months post-harvest resulted in significant decreases in Fp DNA overall; however, long-term survival of Fp was observed 10–20 cm above the crown.

Conclusions. Different residue management scenarios did not increase FCR risk for Year 3, likely owing to high inoculum levels across all treatments and unseasonably wet conditions in Years 2–3.

Implications. We provide important field-validation of Fp colonisation in standing cereal stubble and discuss implications for FCR management across regions and seasons.

Finger millet (Eleusine coracana (L.) Gaertn.) is a climate-resilient C4 cereal crop with exceptional adaptability to arid and semi-arid regions. Its unique morpho-physiological, biochemical, and molecular mechanisms contribute to its high tolerance to drought, making it a valuable model for sustainable agriculture and breeding programmes. This review explored the drought resilience strategies of finger millet, focusing on its genetic traits, physiological adaptations, and molecular mechanisms. Additionally, the study examines its potential for guiding resilient cropping systems and improving drought tolerance in major crops. A comprehensive review of recent research was conducted, analysing morpho-physiological traits such as root architecture, stomatal control, and biochemical responses, including osmolyte accumulation and antioxidant activities. Molecular studies identifying stress-responsive genes and transcriptomic pathways were also evaluated. Finger millet exhibits high water use efficiency, robust root systems, and adaptive morphological traits that enhance drought resilience. Biochemical responses, such as proline and soluble sugar accumulation, mitigate osmotic stress and oxidative damage. Molecular studies identified key drought-responsive genes (EcDehydrin7, EcNAC67, EcbZIP60) and revealed syntenic relationships with Poaceae species, facilitating gene transfer for breeding. Finger millet’s diverse genetic traits and stress-tolerance mechanisms make it an essential resource for improving drought tolerance in major crops and developing climate-smart agriculture. The insights from finger millet can guide breeding programmes and agricultural practices to enhance global food security in the face of climate change.

Context. Phosphorus deficiency is a limiting factor affecting plant growth, development, and yield.

Aims. This study aimed to evaluate the Iranian maize (Zea mays) germplasm in response phosphorus deficiency and identify genomic loci involved in the response.

Methods. Using a maize 600K Single Nucleotide Polymorphism (SNP) array followed by gene network analysis, a genetic analysis of phosphorus uptake of 93 maize genotypes was evaluated in optimal and phosphorus-deficient conditions. After filtering for a minor allele frequency below 10%, 450,133 SNPs were retained to investigate phosphorus uptake efficiency.

Key results. In both optimal and deficient phosphorus states, seven candidate genes were identified that corresponded with disease resistance proteins (e.g. RPM1 and RPP13), cellular component proteins (e.g. RER3), molecular functional protein (e.g. SF3B4), and other proteins including HVA22-like protein c and PPR. Genes RPM1 and RPP13 interacted with RIN genes that act as essential regulators of the plant defence system. The candidate gene HVA22C could interact with other HVA22 genes to protect cells against environmental stresses.

Conclusions. The identified candidate genes play roles in the abscisic acid signalling pathway, mesophyll cell division, plant defence regulation against pathogens, and chloroplast RNA processing. This preliminary study offered valuable insights, but further validation is needed before drawing definitive conclusions.

Implications. There was genetic variability for phosphorus uptake among the Iranian maize germplasm and the identified genes could applied in future breeding programs of maize to better understand the molecular response to phosphorus deficiency in the development of more phosphorus-efficient maize genotypes.