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Context Within-field yield variability affects crop production and management decisions. To understand and manage this variability, different techniques have been deployed to measure and monitor the crops (and soils) at various spatial scales, including manual measurements, harvester-mounted yield monitors, proximal and remote sensing and crop simulation modelling. The value of this increasing data availability to enhance process understanding and on-ground management is unclear. Objective This study aimed to investigate the value of the increasingly available spatial data from different sources to understand important soil-plant processes amenable to improvement in both simulation modelling and for better management decisions for dryland cropping. Methods We collected three types of measurement data (manual sampling, sensed data from satellite and drone, and yield maps) over a 10 ha field and conducted simulations using the process-based soil-plant model APSIM at different spatial scales (varied from 1 m2 up to 10 ha). We assessed the agreement between ground measurements and yield maps, analysed the potential to use remotely sensed vegetation indices to estimate yield, and the scale at which process-based modelling could be reliable. Results Wheat yield extracted from yield map at 1 m2 spatial resolution only explained 30% of the variation in yield measured from 1 m2 manual sampling, with better agreement when data was aggregated to 1 ha strip-scale (R2 = 0.66, NRMSE = 9.1%). Remotely sensed vegetation indices (VI) were better correlated with the yield map when aggregating images to coarse spatial resolution (> 50 m × 50 m), while high-resolution drone VI increased the correlation at finer scales. However, the relationship and the timing of the highest correlation differed between years. APSIM simulated point-based yield measured from manual samples with NRMSE of 19.4%, but it was difficult to capture spatial variation, Context: Within-field yield variability affects crop production and management decisions. To understand and manage this variability, different techniques have been deployed to measure and monitor the crops (and soils) at various spatial scales, including manual measurements, harvester-mounted yield monitors, proximal and remote sensing and crop simulation modelling. The value of this increasing data availability to enhance process understanding and on-ground management is unclear. Objective: This study aimed to investigate the value of the increasingly available spatial data from different sources to understand important soil-plant processes amenable to improvement in both simulation modelling and for better management decisions for dryland cropping. Methods: We collected three types of measurement data (manual sampling, sensed data from satellite and drone, and yield maps) over a 10 ha field and conducted simulations using the process-based soil-plant model APSIM at different spatial scales (varied from 1 m2 up to 10 ha). We assessed the agreement between ground measurements and yield maps, analysed the potential to use remotely sensed vegetation indices to estimate yield, and the scale at which process-based modelling could be reliable. Results: Wheat yield extracted from yield map at 1 m2 spatial resolution only explained 30% of the variation in yield measured from 1 m2 manual sampling, with better agreement when data was aggregated to 1 ha strip-scale (R2 = 0.66, NRMSE = 9.1%). Remotely sensed vegetation indices (VI) were better correlated with the yield map when aggregating images to coarse spatial resolution (> 50 m × 50 m), while high-resolution drone VI increased the correlation at finer scales. However, the relationship and the timing of the highest correlation differed between years. APSIM simulated point-based yield measured from manual samples with NRMSE of 19.4%, but it was difficult to capture spatial v

Three genotypes of wheat grown at two CO2 concentrations were used in a drought experiment, where water was withheld from the pots at anthesis until stomatal conductance (gs) dropped below 10% of the control and photosynthesis (A) approached zero. The genotypes had different leaf area (Gladius < LM19 < LM62) and while photosynthesis and shoot growth were boosted by elevated CO2, the water use and drying rate were more determined by canopy size than by stomatal density and conductance. The genotypes responded differently regarding number of fertile tillers, seeds per spike and 1000 kernel weight and, surprisingly, the largest genotype (LM62) with high water use showed the lowest relative decrease in grain yield. The maximum photochemical efficiency of photosystem II (Fv/Fm) was only affected on the last day of the drought when the stomata were almost closed although some variation in A was still seen between the genotypes. A close correlation was found between Fv/Fm and % loss of grain yield. It indicates that the precise final physiological stress level measured by Fv/Fm at anthesis/early kernel filling could effectively predict percentage final yield loss, and LM62 was slightly less stressed than the other genotypes, due to only a small discrepancy in finalising the drying period. Therefore, Fv/Fm can be used as a proxy for estimating the yield performance of wheat after severe drought at anthesis.

Convolutional neural networks (CNNs) are a powerful tool for plant image analysis, but challenges remain in making them more accessible to researchers without a machine-learning background. We present RootPainter, an open-source graphical user interface based software tool for the rapid training of deep neural networks for use in biological image analysis. We evaluate RootPainter by training models for root length extraction from chicory (Cichorium intybus L.) roots in soil, biopore counting, and root nodule counting. We also compare dense annotations with corrective ones that are added during the training process based on the weaknesses of the current model. Five out of six times the models trained using RootPainter with corrective annotations created within 2 h produced measurements strongly correlating with manual measurements. Model accuracy had a significant correlation with annotation duration, indicating further improvements could be obtained with extended annotation. Our results show that a deep-learning model can be trained to a high accuracy for the three respective datasets of varying target objects, background, and image quality with < 2 h of annotation time. They indicate that, when using RootPainter, for many datasets it is possible to annotate, train, and complete data processing within 1 d.

CONTEXT: Cover crops are widely promoted in temperate regions of the northern hemisphere to replace or reduce fallow periods between cash crops. The reported benefits (improved ground cover, reduced nitrate leaching, increased soil organic matter among others) have prompted widespread interest in cover cropping in other agro-climatic zones, including Mediterranean environments such as southern Australia, where the wet, mild winters and hot dry summers are a stark contrast to temperate environments. OBJECTIVE: This review considers the reported benefits of cover crops in temperate environments in the context of cropping systems in the water-limited, Mediterranean climate in the grain-growing regions of southern Australia. METHODS: Published studies on cover cropping were critically assessed to evaluate whether the reported impacts may be relevant to Mediterranean-type climates. RESULTS AND CONCLUSIONS: Nitrate leaching is not generally problematic in southern Australian cropping systems due to low summer rainfall and high temperatures, and complete retention of crop stubbles can adequately protect the soil from erosion. Further, summer rainfall is low and sporadic which not only limits cover crop planting opportunities but means that water conserved over summer fallows contributes significantly to winter cash crop yield. Consequently, cover crop duration would need to be short (1–2 months) to balance the benefits of increased cover for water infiltration with transpiration loss during cover crop growth. Restricted cover crop biomass production due to water constraints and short fallow length will limit the potential value of the cover crop to increase soil organic matter, microbial activity, and biologically-fixed nitrogen contributions – all reported benefits for higher biomass cover crops in temperate environments. Appropriate tactical use of summer cover crops may arise in situations where (i) soil cover is reduced by winter crop failure or prolonged drought cr

The scale of root quantification in research is often limited by the time required for sampling, measurement and processing samples. Recent developments in Convolutional Neural Networks (CNN) have made faster and more accurate plant image analysis possible which may significantly reduce the time required for root measurement, but challenges remain in making these methods accessible to researchers without an in-depth knowledge of Machine Learning. We analyzed root images acquired from three destructive root samplings using the RootPainter CNN-software that features an interface for corrective annotation for easier use. Root scans with and without non-root debris were used to test if training a model, i.e., learning from labeled examples, can effectively exclude the debris by comparing the end-results with measurements from clean images. Root images acquired from soil profile walls and the cross-section of soil cores were also used for training and the derived measurements were compared with manual measurements. After 200 minutes of training on each dataset, significant relationships between manual measurements and RootPainter-derived data were noted for monolith (R2=0.99), profile wall (R2=0.76) and core-break (R2=0.57). The rooting density derived from images with debris was not significantly different from that derived from clean images after processing with RootPainter. Rooting density was also successfully calculated from both profile wall and soil core images, and in each case the gradient of root density with depth was not significantly different from manual counts. Differences in root-length density (RLD: cm cm-3) between crops with contrasting root systems were captured using automatic segmentation at soil profiles with high RLD (1 to 5 cm cm-3) as well as at low RLD (0.1 to 0.3 cm cm-3). Our results demonstrate that the proposed approach using CNN can lead to substantial reductions in root

We present RootPainter, a GUI-based software tool for the rapid training of deep neural networks for use in biological image analysis. RootPainter facilitates both fully-automatic and semiautomatic image segmentation. We investigate the effectiveness of RootPainter using three plant image datasets, evaluating its potential for root length extraction from chicory roots in soil, biopore counting and root nodule counting from scanned roots. We also use RootPainter to compare dense annotations to corrective ones which are added during the training based on the weaknesses of the current model.

Deeper root growth can be induced by increased biopore density. In this study, we aimed to compare deep root traits of two winter crops in field conditions in response to altered biopore density as affected by crop sequence. Two fodder crop species-chicory and tall fescue-were grown for two consecutive years as preceding crops (pre-crops). Root traits of two winter crops-barley and canola, which were grown as subsequent crops (post-crops)-were measured using the profile wall and soil monolith method. While barley and canola differed greatly in deep root traits, they both significantly increased rooting density inside biopores by two-fold at soil depths shallower than 100 cm. A similar increase in rooting density in the bulk soil was observed below 100 cm soil depth. As a result, rooting depth significantly increased (>5 cm) under biopore-rich conditions throughout the season of the winter crops. Morphological root traits revealed species-wise variation in response to altered biopore density, in which only barley increased root size under biopore-rich conditions. We concluded that large-sized biopores induce deeper rooting of winter crops that can increase soil resource acquisition potential, which is considered to be important for agricultural systems with less outsourced farm resources, e.g., Organic Agriculture. Crops with contrasting root systems can respond differently to varying biopore density, especially root morphology, which should be taken into account upon exploiting biopore-rich conditions in arable fields. Our results also indicate the need for further detailed research with a greater number of species, varieties and genotypes for functional classification of root plasticity against the altered subsoil structure.

Background: Ingrowth-core method is a useful tool to determine fine root growth of standing crops by inserting root-free soil in mesh-bags for certain period of time. However, the root density observed by the method does not directly explain the nutrient uptake potential of crop plants as it varies over soil depth and incubation time. We have inserted an access-tube up to 4.2 m of soil depth with openings directly under crop plants, through which ingrowth-cores containing labelled soil with nutrient tracers were installed, called core-labelling technique (CLT). The main advantage of CLT would be its capacity to determine both root density and root activity from the same crop plants in deep soil layers. We tested the validity of the new method using a model crop species, alfalfa (Medicago sativa) against three depth-levels (1.0, 2.5 and 4.2 m), three sampling spots with varying distance (0-0.36, 0.36-0.72 and > 5 m from core-labelled spot), two sampling times (week 4 and 8), and two plant parts (young and old leaves) under two field experiments (spring and autumn).Results: Using CLT, we were able to observe both deep root growth and root activity up to 4.2 m of soil depth. Tracer concentrations revealed that there was no sign of tracer-leakage to adjacent areas which is considered to be advantageous over the generic tracer-injection. Root activity increased with longer incubation period and tracer concentrations were higher in younger leaves only for anionic tracers.Conclusions: Our results indicate that CLT can lead to a comprehensive deep root study aiming at measuring both deep root growth and root activity from the same plants. Once produced and installed, the access-tubes and ingrowth-cores can be used for a long-term period, which reduces the workload and cost for the research. Therefore, CLT has a wide range of potential applications to the research involving roots in deep soil layers, which requires further confirmation by future experimen

Introduction: Deep roots have the potential to exploit plant resources otherwise inaccessible. The aim of this study was to develop a method, the Core-Labelling Technique (CLT), to measure root activity down to 2.5 m of soil depth under field conditions. Materials and Methods: Two stainless steel ingrowth-cores, 0.1 m in diameter and 0.55 m in length, were filled with soil labelled with nutrient tracers, i.e., LiCO3, CsCO3, Na2SeO4, RbCO3 and 15NH4Cl. The labelled cores were placed into an access-tube having openings at 1.0 m and 2.5 m of soil depth. They were kept under a lucerne (Medicago sativa) crop for 60 days, after which, the root length density (RLD) and concentration of tracers in shoot biomass (including a control) were measured. Results: RLD of lucerne measured at 1.0 m and 2.5 m of soil depth were 0.022 and 0.007 cm cm-3, respec-tively. Effects of core-labelling on shoot samples were significant for 15N, Li, Cs and Se across the soil depth but not for Rb. On average 6 times higher concentration of 15N (‰) was found at the labelled spot compared to the control. Li and Cs (mg kg-1) at the labelled area showed 2 and 3 times higher concentration than the control, respectively. Regardless of the treatment, core-labelling at 1 m of soil depth resulted in higher concentration of 15N and Cs in comparison to 2.5 m of soil depth. Discussion: To our knowledge, this report illustrates the deepest application of an ingrowth-core method as well as its first adoption of the tracer technique. Our results suggest that 15N, Li, Cs and Se can be feasible tracers for root activity detection in arable subsoil. Conclusions: The CLT can be used as an effective tool for determination of root activity in arable subsoil. We suggest that root research should progress beyond the generic depth-scale in order to discover the relevant yet hidden function of deep roots in crops in the field.

Sustaining an increasing human population with decreasing soil resources is a great challenge of the 21st century. Suboptimal availability of water and N are primary limitations to plant growth in the low-input agroecosystems in developing nations, whereas intensive fertilization at the cost of substantial environmental pollution is a problem in rich countries. The development of crops with greater rooting depth addresses these challenges. Deeper rooting improves water and nutrient uptake, which in turn reduces the need of application of external resources. In our study, we investigate the effect of intercropping on the deep root development and nutrient uptake in sugar beet – chicory mixture. The crops were chosen due to their deep roots and similar growing periods. We hypothesize that in the intercropping system one of the component crops develops roots below the root surface of the other crop, which results in deeper nutrient uptake in comparison to crops growing in pure stands. Crops will be grown in the field as monocultures and in mixture. Root growth will be monitored with minirhizotron method up to 4 m depth. Nutrient uptake will be studied using stable isotopes and ingrowth core methods. Additionally, root biomass estimation through DNA extraction from soil will be investigated. We expect that the results will help to answer the questions of whether the intercropping enhances deeper root growth and nutrient uptake in comparison to sole crops. With this knowledge, exploitation of the subsoil resources by deep roots would contribute to enhanced food production in a sustainable way. The study will start in spring 2018 and preliminary results will be presented.

Large areas of salt-affected soils are found in tropics, especially in Africa, South America and the Middle East. While soil salinity might lead to food insecurity in those regions and affect farmers who are most vulnerable to environmental stress, however, little is known how the abiotic stress can be managed with locally available resources without involving high cost. Decades of practical evidence have demonstrated the benefits of the farm input, called biodynamic preparation 500 (P500) cow horn manure, such as improving crop performance and providing resilience against various stress conditions. Organic agriculture is already seen as an important issue in sustainable crop production in the tropics, but also biodynamic philosophy and agricultural practices are discovering tropical countries. Biodynamic preparations in tropical crop production are already in use, while there is a lack of research concerning their performance. This study was conducted to quantitatively determine the effects of P500 on the early growth of different crops under saline condition. A randomised block design with six replicates in a green house chamber pot trial consisting of two factors, viz., SALT (stressed and control) and PREP (P500 and blank) was run with four crop species that have importance in temperate and tropical countries, viz., barley, pea, quinoa and tomato. Plants were harvested 38 days after sowing and aerial biomass dry matter (DM g) was measured. Results suggest that the application of P500 significantly enhanced the initial biomass production of all tested crops, even under saline conditions (except for tomato). In all crops the biomass yield was significantly improved, average 18.5 % and 16.7 % for non-saline and saline treatments, respectively. Findings suggest that P500 may be a measure to mitigate the stress from adverse environmental conditions for crop plants. Thus, future research with more genotypes, varieties, and crop species, in field conditions where the so