Cotton Research and Development Corporation

Abstract The average lint yield of irrigated cotton in Australia ranges from 2270 to 3700 kg/ha, but yields vary substantially between farms and also between fields on the same farm. Differences in soil properties may cause these yield variations. Identifying which factors are causal and what management can be implemented to mitigate the impacts should help optimize inputs and improve profits. During the 2018–2019 summer cotton‐growing season, a paired‐field comparison approach was used to investigate and improve the understanding of soil property‐induced irrigated cotton yield differences within five farms across three regions of NSW, Australia. The paired fields at each farm recorded an average lint yield difference of >284 kg/ha (measured in 2018–2019 or 5‐year average lint yield). Several soil properties differed between the paired fields at each farm comparison. The soil organic carbon stocks were higher in the higher‐yielding fields at all the farm comparisons and the normalized lint yield percentage was positively correlated with soil organic carbon stocks. Soil sodicity was higher in the lower‐yielding fields at 3 of the 5 comparisons. Results for most soil nutrient tests were above the recommended critical concentrations for Australian cotton production. A stepwise linear regression excluding soil nutrients that were above soil test critical values for crop response and below crop toxicity levels indicated the lint yield was positively correlated with SOC stocks and negatively correlated with sodicity and bulk density. No earthworms were detected during visual soil assessment or soil sampling across all the sites. Visual soil assessment was not a sensitive predictor of cotton crop performance. Comparing soil properties using a paired field approach may assist cotton growers in understanding the factors behind yield differences. A similar strip comparison approach could be adopted for within‐field variability by dividing the fields into discrete performance zones and assessing the soil properties of each zone separately.

BackgroundCotton accounts for 80% of the global natural fibre production. Its leaf hairiness affects insect resistance, fibre yield, and economic value. However, this phenotype is still qualitatively assessed by visually attributing a Genotype Hairiness Score (GHS) to a leaf/plant, or by using the HairNet deep-learning model which also outputs a GHS. Here, we introduce HairNet2, a quantitative deep-learning model which detects leaf hairs (trichomes) from images and outputs a segmentation mask and a Leaf Trichome Score (LTS).ResultsTrichomes of 1250 images were annotated (AnnCoT) and a combination of six Feature Extractor modules and five Segmentation modules were tested alongside a range of loss functions and data augmentation techniques. HairNet2 was further validated on the dataset used to build HairNet (CotLeaf-1), a similar dataset collected in two subsequent seasons (CotLeaf-2), and a dataset collected on two genetically diverse populations (CotLeaf-X). The main findings of this study are that (1) leaf number, environment and image position did not significantly affect results, (2) although GHS and LTS mostly correlated for individual GHS classes, results at the genotype level revealed a strong LTS heterogeneity within a given GHS class, (3) LTS correlated strongly with expert scoring of individual images.ConclusionsHairNet2 is the first quantitative and scalable deep-learning model able to measure leaf hairiness. Results obtained with HairNet2 concur with the qualitative values used by breeders at both extremes of the scale (GHS 1-2, and 5-5+), but interestingly suggest a reordering of genotypes with intermediate values (GHS 3-4+). Finely ranking mild phenotypes is a difficult task for humans. In addition to providing assistance with this task, HairNet2 opens the door to selecting plants with specific leaf hairiness characteristics which may be associated with other beneficial traits to deliver better varieties.

Context Production of rainfed (dryland) cotton (Gossypium hirsutum L.) occurs in many places globally, and is always burdened with greater uncertainties in outcomes than irrigated cotton. Assessing farm financial viability helps farmers to make clearer and more informed decisions with a fuller awareness of the potential risks to their business. Aim We aimed to highlight key points of uncertainty common in rainfed cotton production and quantify these variable conditions to facilitate clearer decision-making on sowing dates and row configurations. Methods The consequences of these decisions at six locations across two states in Australia, given estimates of plant-available water at sowing, are expressed in terms of comparable probability distributions of cotton lint yield (derived from crop modelling using historical weather data) and gross margin per hectare (derived from historical prices for inputs and cotton lint yield), using the copula approach. Examples of contrasting conditions and likely outcomes are summarised. Key results Sowing at the end of October with solid row configuration tended to provide the highest yield; however, single- and double-skip row configurations generally resulted in higher gross margins. Places associated with higher summer-dominant rainfall had greater chance of positive gross margins. Conclusion In order to maximise the probability of growing a profitable crop, farmers need to consider the variabilities and dependencies within and across price and yield before selecting the most appropriate agronomic decisions. Implications Given appropriate data on growing conditions and responses, our methodology can be applied in other locations around the world, and to other crops.

Abstract Key management recommendations for cotton (Gossypium hirsutum L.) management require estimates of the timing of crop phenology. Most commonly growing day degree (DD) (thermal time) approaches are used. Currently, across many cotton production regions, there is no consistent approach to predicting first square and flower timing. Day degree approaches vary considerably, with base thresholds different (12.0–15.6 °C) with no consistency using an optimum temperature threshold (i.e., temperature where development ceases to increase). As cotton is grown in variable and changing climates, and cultivars change, there is a need to ensure the accuracy of this approach for predicting timing of flowering for assisting cotton management. In this study new functions to predict first square and first flower were developed and validated using data collected in multiple seasons and regions (Australia and the United States). Earlier controlled environment studies that monitored crop development were used to assess in more detail how temperatures were affecting early cotton development. New DD functions developed predicted first square and first flower better than the existing Australian and U.S. approaches. The best performing functions had base temperatures like those of existing U.S. functions (15.6 °C) and an optimum threshold temperature of 32.0 °C. New universal DD targets for first square (343 DD [°C]) and first flower (584 DD) were developed. Controlled environment studies supported this base temperature outcome; however, it was less clear that 32.0 °C was the optimum threshold temperature from these data. Precise predictions of cotton development will facilitate accurate growth stage assessments and hence better cotton management decisions.

ObjectivesCycling is an important means of transportation in the Netherlands. Unfortunately, the number of cycling accidents and their adverse outcomes (injury and death) are on the rise. We set out to observe the nature of these accidents in Maastricht from 2001 to 2015 and analyzed the recommendations of stakeholders on ways to improve cycling safety.MethodsAn explanatory sequential mixed methodology was used for this population-based study. In the first phase, a retrospective quantitative analysis of the VIA® accident database for Maastricht was done. This was followed by a thematic analysis of data from five semi-structured interviews. Integration was at the Interpretation stage.ResultThe first phase showed males (54%) and under-25s (59.9%) had the most cycling accidents, while a larger percentage of females (50.7%) and people >65 years (67%) had adverse outcomes with accidents. More accidents occurred at intersections (52.6%), on shared roads (61.4%), and involved motorized vehicles (95.6%). Bivariate analysis and multivariable logistic regression showed that cycling accidents involving elderly people, women, wet weather or road surfaces, an innocent cyclist, the northeastern district, and morning hours had a higher chance of injury or death. Thematic analysis summarised stakeholder opinions under four themes: role in cycling safety; partners of cycling safety; the importance of accurate data; and investing in safety. Most of the respondents felt improvements in the city’s accident database, cycling policy, and infrastructure were needed.ConclusionOur findings suggest that there has been a decrease in the overall number of cycling accidents in the 15-year period studied. However, differences in sociodemographic variables still determine the distribution and severity of accidents in Maastricht. The existing cycling database at the time of the study needed improvements with data collection and the city needs to involve more stakeholders in its policy-making process.

Faba bean (Vicia faba L.), a member of the Fabaceae family, is one of the important food legumes cultivated in cool temperate regions. It holds great importance for human consumption and livestock feed because of its high protein content, dietary fibre, and nutritional value. Major faba bean breeding challenges include its mixed breeding system, unknown wild progenitor, and genome size of ~13 Gb, which is the largest among diploid field crops. The key breeding objectives in faba bean include improved resistance to biotic and abiotic stress and enhanced seed quality traits. Regarding quality traits, major progress on reduction of vicine‐convicine and seed coat tannins, the main anti‐nutritional factors limiting faba bean seed usage, have been recently achieved through gene discovery. Genomic resources are relatively less advanced compared with other grain legume species, but significant improvements are underway due to a recent increase in research activities. A number of bi‐parental populations have been constructed and mapped for targeted traits in the last decade. Faba bean now benefits from saturated synteny‐based genetic maps, along with next‐generation sequencing and high‐throughput genotyping technologies that are paving the way for marker‐assisted selection. Developing a reference genome, and ultimately a pan‐genome, will provide a foundational resource for molecular breeding. In this review, we cover the recent development and deployment of genomic tools for faba bean breeding.

Soils provide the substrate for important microbial mechanisms that moderate a variety of processes in both managed and natural terrestrial ecosystems. Australian soils are particularly ‘stressed’ and are considered to be highly weathered and nutrient deficient. This places increased pressure on the Australian cotton industry to sustainably increase productivity to support the fibre demands of a growing global population. We explored Vertosol subsoils (>30 cm) under rotational cotton crops, measuring the distribution of soil microbial activity (SMA) and biomass (SMB) to 100 cm depth, as influenced by crop system and time, using respiration-based experiments. Seasonal SMA fluctuations were considered by capturing the long-term SMA between systems using stable oxygen isotope methodology. Our results indicate that subsoils contributed 47% of SMA, regardless of system, but subsoil SMB (26%) was constrained by resource availability. Long-term SMA was not significantly influenced by depth and system, presumably as a consequence of the shrink–swell nature of Vertosols facilitating organic matter movement down the profile. The impact of this organic matter on the activity of indigenous microorganisms below the top 30 cm of the profile highlights a need to consider deeper soil when unravelling the potential microbial benefits to our cropping systems.

Runoff monitoring experiments are often conducted in remote sites. Sample collection and dispatch for analysis are often delayed due to sites’ remoteness and limited local laboratory facilities. The standard method of dissolved organic carbon (DOC) and total organic carbon (TOC) analysis in water samples requires storage of the samples at 4 °C after collection and analysis with a minimum of delay. However, there is no freezing storage time limit specified to avoid degradation. To overcome the limitations of this approach, we investigated the method of storage, that is refrigeration (4 °C) versus freezing (-18 °C), and the effect of storage time on DOC and TOC fractions in runoff water. Storage of samples at 4 °C for more than a week resulted in a decline of TOC and DOC concentrations in runoff water.•Freezing unfiltered water samples immediately after collection minimized TOC losses during storage, however, it may lead to variable DOC results.•Filtering a subsample of runoff or irrigation water immediately after collection using a 0.45 µm filter and freezing both the filtered and unfiltered samples until analysis of DOC and TOC, respectively, can minimize losses during storage.