Full-Season Cover Crops and Their Traits That Promote Agroecosystem Services

Non-marketable crops are increasingly being used as a tool to promote agroecosystem services and sustainable agriculture. Nevertheless, crops vary greatly in the traits by which they capture resources and influence the local ecosystem. Here we report on the traits and associated soil microbial communities that relate to aboveground biomass production, nutrient capture, weed suppression, erosion control and building particulate organic matter of 22 different full-season cover crops. All agroecosystem services were positively correlated with maximum canopy height and leaf area. Rooting density was positively associated with indices of bacterial diversity. While some legumes produced the greatest standing N and P in aboveground biomass, they were also poor at capturing soil nitrate and promoted high levels of potential plant fungal pathogens. Conversely, Brassicaceae crops had the lowest levels of potential plant fungal pathogens, but also suppressed saprophytic fungi and rhizobia. Thus, not all crops are equal in their ability to promote all agroecosystem services, and while some crops may be ideal for promoting a specific agroecosystem service, this could result in a trade-off with another. Nonetheless, our study demonstrates that plant functional traits are informative for the selection of crops for promoting agroecosystem services.

Digging roots is easier with AI

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 (R 2=0.99), profile wall (R 2=0.76) and core-break (R 2=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 sample processing workloads, increasing the potential scale of future root investigations.

Biopore-Induced Deep Root Traits of Two Winter Crops

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.

Digging roots is easier with AI

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. Our results demonstrate that the proposed approach using CNN can lead to substantial reductions in root sample processing workloads, increasing the potential scale of future root investigations.

Rooting density differentiates wheat genotypes through Bayesian modeling

Wheat pre-breeders use soil coring and core-break counts to phenotype root architecture traits, with data collected on rooting density for hundreds of genotypes in small increments of depth. The measured densities are both large datasets and highly variable even within the same genotype, hence, any rigorous, comprehensive statistical analysis of such complex field data would be technically challenging. Traditionally, most attributes of the field data are therefore discarded in favor of simple numerical summary descriptors which retain much of the high variability exhibited by the raw data. This poses practical challenges: although plant scientists have established that root traits do drive resource capture in crops, traits that are more randomly (rather than genetically) determined are difficult to breed for. In this paper we develop a Bayesian hierarchical nonlinear modeling approach that utilizes the complete field data for wheat genotypes to fit anidealizedrelative intensity function for the root distribution over depth. Our approach was used to determineheritability: how much of the variation between field samples was purely random versus being mechanistically driven by the plant genetics? Based on the genotypic intensity functions, the overall heritability estimate was 0.62 (95% Bayesian confidence interval was 0.52 to 0.71). Despite root count profiles that were statistically very noisy, our Bayesian analysis led to denoised profiles which exhibited rigorously discernible phenotypic traits. The profile-specific traits could be representative of a genotype and thus can be used as a quantitative tool to associate phenotypic traits with specific genotypes.

Soil solution composition in association with the toxicity of banded di-ammonium phosphate to wheat and amelioration by CaCO3

Our previous publications showed that gradients of pH, electrical conductivity, ammonium, phosphorus, and calcium were formed between di-ammonium or mono-ammonium phosphate bands and roots. These gradients shifted and diminished with time. Roots suffered from ammonia toxicity near the band, but soil liming before banding ameliorated the toxicity. In the present study, DAP was banded 1 cm away from wheat (Triticum aestivum) seeds sown in slightly acidic sandy Lancelin soil that was either limed (CaCO3) or not. After 35 days, the pH and concentration of 9 ions were measured in soil solution extracted from soil obtained at different distances between the fertiliser band and seed.Toxicity symptoms were noted on 7-day-old plants grown in the non-limed treatment; in contrast, plants grown in the CaCO3 treatment did not show these symptoms during the whole growth period. In comparison with the non-limed treatment, CaCO3 addition markedly lowered the ammonium and P concentration in soil solution extracted from soil between the fertiliser band and the seed. Although a lower Ca concentration was measured in the vicinity of the DAP band in the non-limed than in the limed treatment, Ca in non-limed soil was still sufficiently high to prevent deficiency in plants, implying that there might be no ground for the association of an injurious effect of DAP and Ca deficiency as suggested in other studies. Around 2.8 mg Al/L soil solution was detected in the non-limed treatment, but liming with CaCO3 reduced Al concentration in all soil sections, especially those with the high rooting density. Therefore, a possibility that Al toxicity was related to the DAP toxicity in non-limed soils cannot be excluded, considering that even higher Al would have existed in the soil solution in the vicinity of the fertiliser band during the first couple of days. In conclusion, the causal factors associated with DAP toxicity might be high concentration of ammonium and free ammonia resulting from hydrolysis of DAP, and high P and possibly high Al concentrations.

Ectomycorrhizal community structure on western hemlock (Tsuga heterophylla) seedlings transplanted from forests into openings

This study tested whether mature-forest ectomycorrhizal (ECM) communities could be maintained in forest openings on seedlings. Naturally regenerated western hemlock (Tsuga heterophylla (Raf.) Sarg.) seedlings were transplanted from mature forests into openings and the ECM fungal community was compared after 2 years with similar seedlings planted back into the forests or seedlings from openings planted back into openings. Fewer ECM morphotypes, lower average richness per seedling, and a steeper, less even species distribution curve were found, all of which suggest that the mature-forest ECM fungal community changed after transplanting forest seedlings into the openings. The increased abundance of pioneer fungi such as Thelephora terresteris suggested that many of the mature-forest ECM fungi were unable to maintain or continue root colonization in openings. Results suggest that many mature-forest ECM fungi require further stand development to maintain enough rooting density and hyphal contact to persist.Key words: ectomycorrhizal succession, disturbance, species-importance curves, multistage and late-stage fungi.

Crop production and water-use. I. A model for estimating crop water-use with limited data

SummaryAn attempt was made to re-assess the results of irrigation experiments carried out in the UK during the last 30–40 years in order to develop functional relationships between crop yield and actual wateruse. It was first necessary to develop a model, based on relatively simple functions, which could be used to predict actual water-use (evaporation and transpiration). The derivation of this model, known as WATYIELD, is reported here, whilst its application and validation for sugarbeet and for potatoes are described in subsequent papers. Simple functions relate components of evaporation to the proportion of the incoming solar radiation intercepted by the crop canopy, reference crop evapotranspiration, the precipitation and irrigation amounts and the soil water deficit. Transpiration is also related to soil hydraulic characteristics, rooting density and potential evaporation rate.