Agronomic Field Research Articles

A Framework for Visualization and Analysis of Agronomic Field Trials from On‐Farm Research Networks

An on‐farm research network is an organization of farmers that conducts agronomic experiments under local conditions. It is common that an elementary statistical analysis be conducted for individual studies. However, there is unexplored potential in detecting yield response variability patterns for better decision making. We developed a data‐analytics framework and web‐application program that allows users to analyze multiple studies that use a common protocol and can identify the conditions where an imposed treatment may or may not be effective. The development of this data‐analytics framework is needed to improve predictions at the farm level that can lead to more cost‐effective, sustainable and environmentally sound agricultural production. Data visualization is an important part of data‐analytics. In this paper, we have developed and tested a Bayesian hierarchical model that can be used to assess the general agronomic performance of different management practices. Decision making related to new management practices should be based on the complete evidence, local conditions and economic considerations. The web‐application includes dynamic data visualization features to enhance communication and sharing of information with the goal to reach a broader audience.Core Ideas We develop a data‐analytics framework and web‐application for on‐farm research trials. A Bayesian hierarchical model quantifies the uncertainty in yield response. The model helps assess alternative practices, products, and technologies among trial locations. The framework provides a reactive break‐even economic analysis of alternative management practices.

Solar-induced chlorophyll fluorescence as an indicator for determining the end date of the vegetation growing season

Abstract In this study, the performance of solar-induced chlorophyll fluorescence (SIF) in detecting the end date of the vegetation growing season (EGS) is evaluated at the canopy level. The experiment was conducted at two ecological stations in the Olympic Park mixed forest and Yucheng cropland in China. Validated against the gross primary productivity (GPP) measured by the eddy covariance technique (GPPEC), the SIF was first compared with the normalized difference vegetation index (NDVI). The results showed that at the Olympic Park station (mixed forest), the EGS of SIF was 4 days earlier than that of the GPPEC, and the EGS of the NDVI showed 2 days of hysteresis. However, in the Yucheng cropland, the EGS of the NDVI was 26 days later than that of the GPPEC, while SIF lagged by 18 days. Considering the total biases against the EGS of GPPEC, the SIF was comprehensively superior to the NDVI for determining the end date of the vegetation growing season in these two ecosystems. SIF was further incorporated into a light use efficiency model to estimate the GPP, which was also used to extract the EGS. Two sets of inputs were employed, including the photochemical reflectance index and SIF, apparent fluorescence yield and SIF. The results showed that this SIF-based light use efficiency model can yield good estimations of GPPs, with correlation coefficients of all GPP values being above 0.8 for both ecosystems. Additionally, the estimated end dates of the vegetation growing season were better than those from the single SIF proxy. In particular, the combination of the two fluorescence variables used in the light use efficiency model contributed the best performance in that the EGS was 2 days earlier than that of the GPPEC at Olympic Park station and 13 days later than that of the GPPEC at Yucheng station. The EGS was improved by 2 days and 5 days for the mixed forest site and cropland site, respectively, compared to the EGSs determined by SIF. The influences of air temperature and sunlight availability on the temporal patterns of the GPP, SIF and NDVI was investigated. Light was the main factor controlling the GPP and SIF, while the NDVI was primarily controlled by the air temperature in both ecosystems. These results explained the different mechanisms of the three surrogates, and therefore, the distinct performances in detecting the EGSs of plants was explained. Our findings suggest that SIF is suitable for determining the end date of the vegetation growing season, regardless of whether it is used alone or in combination with other photosynthetic indices in mixed forest and cropland ecosystems. The errors in the estimated EGS can be limited to twenty days. Based on the results of this study, SIF can be more widely employed either in the ecological field to investigate vegetation dynamics and carbon cycling or in the agronomic field to predict the harvest period and adjust agricultural management strategies.

Sustainable Agronomic Strategies for Enhancing the Yield and Nutritional Quality of Wild Tomato, Solanum Lycopersicum (l) Var Cerasiforme Mill

Urbanization and global climate change have constrained plant development and yield. Utilization of wild gene pool, together with the application of sustainable and eco-friendly agronomic crop improvement strategies, is being focused on to tackle mounting food insecurity issues. In this aspect, the green seaweed, Ulva flexuosa, was assessed for plant biostimulant potential on cherry tomato, in terms of seed priming effects, nutrition and yield. SEM-EDX analysis of U. flexuosa presented the occurrence of cell wall elements (O, Na, Mg, S, Cl, K and Ca). The phytochemical analyses of liquid seaweed extract (EF-LSE) revealed the presence of carbohydrates, protein, phenols, flavonoids, saponins, tannins and coumarins. The EF-LSEs were found to stimulate seed germination in a dose-dependent manner, recording higher seed germination, and biomass and growth parameters. The seedlings of treated seeds altered the biochemical profile of the fruit, in terms of TSS (93%), phenol (92%), lycopene (12%) and ascorbic acid (86.8%). The EF-LSEs positively influenced fruit yield (97%). Henceforth, this investigation brings to light the plant biostimulant potential of the under-utilized seaweed source, U. flexuosa, to be useful as a bio fertilizer in agronomic fields for a cumulative enhancement of crop vigour as well as yields to meet the growing food demands.

Herbicide Resistance Management: Recent Developments and Trends.

This review covers recent developments and trends in herbicide-resistant (HR) weed management in agronomic field crops. In countries where input-intensive agriculture is practiced, these developments and trends over the past decade include renewed efforts by the agrichemical industry in herbicide discovery, cultivation of crops with combined (stacked) HR traits, increasing reliance on preemergence vs. postemergence herbicides, breeding for weed-competitive crop cultivars, expansion of harvest weed seed control practices, and advances in site-specific or precision weed management. The unifying framework or strategy underlying these developments and trends is mitigation of viable weed seeds into the soil seed bank and maintaining low weed seed banks to minimize population proliferation, evolution of resistance to additional herbicidal sites of action, and spread. A key question going forward is: how much weed control is enough to consistently achieve the goal of low weed seed banks? The vision for future HR weed management programs must be sustained crop production and profitability with reduced herbicide (particularly glyphosate) dependency.

Evidence for relative grain yield simulation by red color level of beneath-canopy soil at wheat booting phase: An unexpected observation using image processing

Digital cameras are increasingly utilized as a reliable and readily available remote sensing tool in the emerging technology of high throughput phenotyping and agronomic field assessments. In almost every image analysis of green canopies, image segmentation into the vegetation and non-vegetation parts using thresholding is a fundamental step. Therefore, only the vegetation parts of the high resolution images are considered for estimations and the data recorded in the remained pixels will be ignored. In the present study, an accidental observation is described, in which the red color level of the non-vegetation segments of the images taken at the wheat booting phase could simulate the relative pattern of final grain yield. An image archive of heterogeneous wheat canopies (i.e. cultivar mixtures), captured from experimental plots of a field study conducted at the School of Agriculture, Shiraz University, Iran, was analyzed. It was observed that the red color levels of the image background (BRL, mostly composed of soil pixels) were accurately simulating the final grain yields of the treatments, (i.e. very precise mimicry of relative grain yields –RGY– in monocultures, and exact determination of curve extrema and also the direction of fluctuations, in the case of mixtures). Compared with other colors and image-derived criteria evaluated, BRLs had the pattern most similar to the relative grain yields. Moreover, assessments showed the importance of using higher number of replicates in analyses (i.e. 6 vs 3 replications). This observation suggests the red color level of the image-background as a potential novel index for the prediction of wheat grain yield during the preanthesis period; and also the ability of the non-vegetation image segments to provide valuable data for crop remote sensing and image processing studies.

An Automatic Field Plot Extraction Method From Aerial Orthomosaic Images.

Unmanned aerial vehicles have an immense capacity for remote imaging of plants in agronomic field research trials. Traits extracted from the plots can explain development of the plants coverage, growth, flowering status, and related phenomenon. An important prerequisite step to obtain such information is to find the exact position of plots to extract them from an orthomosaic image. Extraction of plots using tools which assume a uniform spacing is often erroneous because the plots may neither be perfectly aligned nor equally distributed in a field. A novel approach is proposed which uses image-based optimization algorithm to find the alignment of plots. The method begins with a uniformly spaced grid of plots which is iteratively aligned with regions of high vegetation index, i.e., the underlying plots. The approach is validated and tested on two different orthomosaic images of fields containing wheat plots with simulated and real alignment problems, respectively. The result of alignment is compared to manually located ground truth position of plots and the errors are quantitatively analyzed. The effectiveness of the proposed method is confirmed in accurately estimating the phenotypic trait of canopy coverage compared to the common methods of extraction from uniform grids or trimmed grids. The software developed in this study is available from SourceForge, https://sourceforge.net/projects/phenalysis/.

Genome Editing as a Patentable Technology:A Future Debacle?

Genome engineering by targeted editing is a revolutionary strategy for biological hacking which has attracted worldwide scientific attention. The technology, though in its naive state has already exhibited a huge potential in directing the progress of medical and agronomic fields. This has led to international legal clashes among research institutions in order to grasp the lucrative intellectual rights over such cash rich synthetic biology. However, commercialization of artificial genome manipulation techniques by breaching the ethical guidelines can have detrimental impacts on the quality of human life and cause social cataclysms. In this article, we have portrayed the crucial biosafety issues associated with genome editing and have also related the current global scenario regarding the patentability of this ‘unpredictable’ technology to the future of scientific advancements.

Phenotypic and molecular characterisation of novel Vicia faba germplasm with tolerance to acetohydroxyacid synthase‐inhibiting herbicides (AHAS) developed through mutagenesis techniques

Faba bean (Vicia faba L.) is an important crop in Australian farming systems, however, weed control is a major constraint due to a lack of in-crop broadleaf herbicide options. To address this, we developed acetohydroxyacid synthase (AHAS) inhibitor herbicide tolerance in faba bean using mutagenesis techniques. Dose-response experiments, agronomic field evaluation and DNA sequencing of the AHAS gene were used to quantify and validate tolerance traits. Four M2 faba bean single-plant biotypes (IMI-1, IMI-2, IMI-3 and IMI-4) at a frequency of 3.63 × 10-6 were successfully recovered. Molecular characterisation of the AHAS gene identified two known target site mutations (resulting in protein substitutions Ala205Val and Ser653Asn) conferring tolerance. Phenotypic characterisation found that both mutations conferred high levels of tolerance to the imidazolinone herbicide imazapyr. However, although the Ala205Val substitution showed improved levels of cross-tolerance to a range of sulfonylurea chemistries, the Ser653Asn substitution did not. In the field, IMI-3 showed the highest level of agronomic tolerance across a range of imidazolinone herbicides. Mutagenesis techniques were successful in the development of tolerance to AHAS inhibitor herbicides in faba bean, and could facilitate the first safe in-crop broadleaf herbicide control option in Australian faba bean production. © 2019 Society of Chemical Industry.

Scaling up research using GIS and WebGIS spatial tools: case study of MicroVeg project

Sustainable intensification of agriculture is a pressing issue for the countries of Benin and Nigeria. Amaranth (Amaranthus cruentus L.), solanum (Solanum macrocarpum L.), fluted pumpkin (Telfairia occidentalis f. Hooke), and parsley (Ocimum gratissimum L.) have been identified as important indigenous vegetables with the potential to enhance food and household security in West Africa. An international collaboration known as MicroVeg was established in 2015 to scale up the production and consumption of these nutritious vegetables. Traditional agronomic field research has a relatively small spatial footprint; by using a geographic information system (GIS), we expanded the reach of MicroVeg research to a regional scale. A GIS database allows for data to be represented by varying methods and to create larger inference spaces. With GIS, we can use the data to infer areas which are similar to the characteristics of a site. GIS data were stored, analyzed, compared, and extrapolated to infer where scaling up of the MicroVeg agronomic package is likely to be most successful. GIS has enabled this project to expand potential areas from several research sites with an approximate total area of 50 hectares, to a potential extent exceeding 100,000 square kilometres for each crop, independently of seasonal variation. As it is a challenge to disseminate such information to audiences, we developed and implemented an interactive, online, database (termed webGIS) of agronomic trial data designed for use with minimal GIS knowledge. The results of this project will be important for policymakers and agronomists who are scaling up vegetable production in Benin and Nigeria.

Field associations of first generation densities of the pest mites Halotydeus destructor and Penthaleus major in pasture.

Halotydeus destructor and Penthaleus major are species of earth mite commonly found at high densities in agricultural fields in Australia and other parts of the world. These mites pose a risk to a range of winter crops and pastures when seedlings emerge in autumn. In order to predict likely mite pressure, we investigated whether autumn densities in pastures can be determined from agronomic and environmental field variables. For H. destructor, field densities showed little association with a range of vegetation variables but could largely be explained using the variable field type, with high densities present when fields had mixtures of grass, clover and weeds. For P. major, we found a regional effect. In the region where most data were available, P. major field densities were associated with grass abundance, whereas an association with field type was significant but different to that found for H. destructor. For both species, densities were not associated with rainfall, but there was a weak association with soil moisture capacity. We discuss how these results can help in managing these important pest mites.

Influence of new effective allelochemicals on the distribution of Cleome arabica L. community in nature

New allelochemicals were identified through bio-guided fractionation from the ethyl acetate of seeds extracts, which was the most autotoxic compared to the other plant parts. Phytochemical investigation of the seeds extracts of C. arabica by spectroscopy analyses has led to identify two new dammarane type triterpenes (4 and 9), with nine known analogues (1 − 3, 5 − 8, 10 and 11), a new cucurbitane triterpene (12), acylated dehydrodiconiferyl alcohol (13), and three highly oxygenated flavonols (14−16). The most autotoxic compounds on germination and seedling growth were elucidated as dammarane type triterpenes. However, less autotoxic effect was recorded by an inhibition under 50% for most of the identified flavonoids. These results suggest that those autotoxic substances may be used as a new bio-herbicide that may contribute to manage the distribution of C. arabica in agronomic field.

Conservation agriculture in western China increases productivity and profits without decreasing resilience

This study examined the economic and risk effects of conservation agriculture (CA) in western China using nine years of data from an agronomic field experiment and a case study for a typical crop-livestock farm. A CA system of a wheat-pea rotation with no-tillage and stubble retention was compared with the current practice of the same rotation but with conventional tillage and stubble removal. Risk was examined by computing the resistance and resilience of grain yields to climate shocks at the field scale, along with calculating the stability of yields and profits at the field and farm scale. Climate shocks were defined using the standardized precipitation evapotranspiration index. Resistance indicated the proximity of grain yields during a climate shock to average yields. Resilience indicated the rate of return towards average grain yields after a climate shock. At the field scale, CA increased grain yields by an average of 19.6% and increased profits without negatively affecting resistance or resilience. At the farm scale, CA increased profits by an average of 5%, increased the stability of profits by 33%, and reduced labor demands. Despite these gains, the adoption of CA in western China remains low. Results suggest that at present the private gains to farmers are not large enough to encourage the more widespread adoption of CA. Therefore, because CA also produces public benefits for conservation of the environment such as reduced erosion, financial incentives may be considered to assist CA adoption in specific farming systems.

What Is the Value of Wild Bee Pollination for Wild Blueberries and Cranberries, and Who Values It?

Pollinator conservation efforts and growing interest in wild bee pollination have increased markedly in the last decade, making it increasingly important to have clear and practical estimates of the value of pollinators to agriculture. We used agricultural statistics, socio-economic producer surveys, and agronomic field research data to estimate traditional pollination value metrics and create novel approaches to the valuation of the ecosystem services provided by wild pollinators. Using two regionally important United States (USA) crops—Maine wild blueberry and Massachusetts cranberry—as models, we present the perceived values of wild bee pollinators from the perspectives of both consumers and producers. The net income attributable to wild bees was similar for wild blueberry ($613/ha) and cranberry ($689/ha). Marginal profit from incrementally adding more hives per ha was greater from stocking a third/fourth hive for cranberry ($6206/ha) than stocking a ninth/10th hive for wild blueberry ($556/ha), given the greater initial responsiveness of yield, revenue, and profit using rented honey bee hives in cranberry compared with wild blueberry. Both crops’ producers were willing to annually invest only $140–188/ha in wild pollination enhancements on their farms, justifying government financial support. Consumers are willing to pay ≈6.7 times more to support wild bees than producers, which indicates a potential source for market-based subsidies for invertebrate conservation.

Sustainable Intensification in Dryland Cropping Systems—Perspectives for Adaptions across the Western Siberian Grain Belt

The Western Siberian grain belt is of global significance in terms of agricultural production as well as carbon sequestration and biodiversity preservation. Regional downscaling of general circulation models predict increasing drought risks and water scarcity for this area. Additionally, significant land-use changes took place in this region after the dissolution of the USSR and collapse of the state farm system: Land-use intensity in Western Siberia (Russian Federation) continuously decreased on grassland, whilst on cropland the intensity increased through recultivation of abandoned cropland and rising fertilizer inputs since 2003. Together, these changing conditions have led to challenges for sustainable agriculture in this semi-arid environment. For sustainable land management, strategies for adapted crop production systems are needed. In agronomic field trials, the potential of enhanced water use efficiency as contribution to a resilient agricultural system under changing climate conditions was evaluated and related to the common practice and regional research. In participatory on-farm trials, higher average soil water content (+40%) in the top soil layer led to higher grain yield (+0.4 t ha−1) and protein yield (+0.05 t ha−1) under no-till compared to the common practice of conventional tillage. Despite this, regional research still promotes bare fallowing with beneficial effects only in the first harvest after fallow, whereas the potential of no-till was visible each year, even under above-average wet and cool growing conditions. In this case study from the Western Siberian grain belt, we depict a possible pathway to make cereal production in Western Siberia more sustainable. However, the approach of applied sustainable intensification by promoting no-till is related to the negative concomitant effect of increased herbicide applications. Due to the strict rejection of GMOs in Russian agriculture by the federal government, this is a great opportunity to maintain a large, pristine area of over 17 million km2 with a lower risk of glyphosate-dependent cropping systems.

Estimation of the optimal plot size and number of replications in a field pepper crop experiment with varying irrigation depths and application frequencies

Abstract The precision of an agronomic field trial, or its ability to detect true treatment effects, can be improved by using both plots of suitable size and an appropriate number of replicates. The objective of this work was to determine the optimal size and number of replications of the experimental plots, by examining differences in plant height, stem diameter and number of fruits per plant, of a bell pepper (Capsicum annuum L.) crop that was irrigated with different water heights and frequencies of application. Five treatments were compared: T0, control with no irrigation; T1, water depth 50% of crop evapotranspiration (ETc) and daily frequency; T2, water depth 100% of ETc and daily frequency; T3, water depth 50% of ETc and frequency every two days; and T4, water depth 100% of ETc and frequency every two days. In the experimental plots, 4 m wide × 12 m long (48 m2), a total of 120 bell pepper (cultivar ‘Impacto’) plants were arranged in four rows. Plants in each row were irrigated with a drip irrigation system with drippers placed between them 0.2 m apart and having a flow rate of 0.8 L h−1. The growth rates of the pepper crop and the number of fruits per standing plant were lower in the treatment without irrigation than in the irrigated treatments. Among the irrigated treatments, daily irrigation provided higher growth rates when the lowest depth of water was applied. In the plants irrigated every two days the highest growth rates were observed in those that received the largest water depth. The number of fruits per standing plant was greater in the daily irrigated plants than in those irrigated every two days, regardless of the amount of the water applied. The optimal plot size was determined by the modified maximum curvature method (MMC) and the linear model with plateau effect (LPR). For each of the three variables, the criterion followed to establish the optimal plot size was to choose the largest one, regardless of whether the method used in its determination was MMC or LPR. The size of the experimental plot was always smaller in the case of treatments with irrigation than in the treatment without irrigation, as well as for the crop variables exhibiting lower variability. Therefore, the optimal sizes of the experimental plots for the plant stem height and diameter, and for the number of fruits per plant was estimated at 3.2 m2 (8 plants); 3.6 m2 (9 plants); and 10.8 m2 (27 plants), respectively. Regardless of the experimental design, complete randomized or complete randomized block, the number of replications of the experimental plots was conditioned by the variability of the three variables. Thus, 10 replicates were required to detect between the five irrigation treatments differences of 10% in the mean value of plants height, 5 replicates in the case of the stem diameter of the plants and 15 replicates for the number of fruits per plant.

Adoption and Impact Assessment of Improved Technologies of Potato Crop Production Systems in Khartoum State, Sudan (2009-2010)

In Sudan, there is increasing concern about improving agricultural production to meet the demand of rapid population growth and boost farmers' profits. Potato is one of the most potential crops that can meet this purpose. This paper applied logistic regression model to assess the main recommended technical package influencing the yield and income in Khartoum state, Sudan. Data for the paper were collected from a total of 192 farmers distributed over four major villages specializing in potato production through structured interview questions, interactive group discussion and personal observation. The results showed that formal finance, informal finance, extension services, potato income, and land ownership have a significant positive impact on the wellbeing of potato farmers of the investigated areas. Adoption of recommended agronomic field practices by a wide number of farmers needs to practically be demonstrated in the field by the authorized institution to show the feasibility of these techniques and technologies.

A Combined Approach of Sensor Data Fusion and Multivariate Geostatistics for Delineation of Homogeneous Zones in an Agricultural Field.

To assess spatial variability at the very fine scale required by Precision Agriculture, different proximal and remote sensors have been used. They provide large amounts and different types of data which need to be combined. An integrated approach, using multivariate geostatistical data-fusion techniques and multi-source geophysical sensor data to determine simple summary scale-dependent indices, is described here. These indices can be used to delineate management zones to be submitted to differential management. Such a data fusion approach with geophysical sensors was applied in a soil of an agronomic field cropped with tomato. The synthetic regionalized factors determined, contributed to split the 3D edaphic environment into two main horizontal structures with different hydraulic properties and to disclose two main horizons in the 0–1.0-m depth with a discontinuity probably occurring between 0.40 m and 0.70 m. Comparing this partition with the soil properties measured with a shallow sampling, it was possible to verify the coherence in the topsoil between the dielectric properties and other properties more directly related to agronomic management. These results confirm the advantages of using proximal sensing as a preliminary step in the application of site-specific management. Combining disparate spatial data (data fusion) is not at all a naive problem and novel and powerful methods need to be developed.

Forage management to improve on-farm feed production, nitrogen fluxes and greenhouse gas emissions from dairy systems in a wet temperate region

Improving forage cropping systems and grasslands are key factors to enhance on-farm resources and sustainability in wet temperate regions of North Spain, contributing to the preservation of associated ecosystem services. This study evaluates the potential of agronomic field management for mitigating greenhouse gas emissions (GHG) and enhancing nitrogen (N) fluxes that can support an increase in on-farm forage resources, thus reducing the dependency on external inputs (fertilizers and feed products). A survey conducted in a weighted sample of 40 dairy farms in Cantabria showed four characteristic forage systems according to field management based on grazing, zero-grazing, conserved forages and growth of maize. The semi-dynamic whole farm model FarmAC was used to characterize a model farm representing an average farm in each of the forage systems including field area and use, number of cows and heifers, diet, milk yield and slurry management. The model was applied to simulate carbon (C) and N fluxes at the farm level, and to calculate feed balances, GHG emissions and the N surplus. Farms were simulated under current forage management (baseline) and under scenarios of enhanced forage production. Milk yield, the balance between forage production and consumption in the animal diet, and between manure generation and application in the field, were used as reference for accepting model simulations. The results from the scenarios indicate that increasing forage productivity, not only reduces the external dependence for feeding animals, but also would have a clear potential for mitigating yield-scaled farm GHG emissions. However, this potential appears to have a limit when N surplus exceeds a threshold value. Rotational grass-clover would have additional benefits in terms of reduced N fertilizer costs and soil carbon enhancement.

Seed Dormancy and Seed Morphology Related to Weed Susceptibility to Biofumigation

Biofumigation is practiced to control soilborne pests and weeds in agronomic fields. The objectives of this research were to assess the dose response of weed seeds to Indian mustard biofumigation and associate responses to seed dormancy state, initial dormancy, and seed parameters. A petri dish biofumigation methodology was developed to expose seeds of common lambsquarters, bird vetch, wild carrot, common ragweed, green foxtail, velvetleaf, hairy galinsoga, and red clover to allelochemicals produced after rehydrating 0 (control), 1.94, 2.90, 5.81, 11.61, and 17.41 mg cm−2of dried mustard powder. Weed species expressed specific dose responses, estimated ED50, LD50, and maximal mortality. Hairy galinsoga and wild carrot were consistently the most affected by biofumigation, with maximal mortality reaching 97% and 95%, ED50values for germination were 1.91 and 2.68 mg cm−2, and LD50values were 3.31 and 3.69 mg cm−2of dried mustard tissue, respectively. Initial dormancy was assessed by germination and tetrazolium tests. Seed parameters such as testa thickness, relative weight of the testa, and seed size were measured directly by manual dissection, weighing seed structures, and stereomicroscopic imaging software measurements. Linear regression analyses revealed initial dormancy to be positively related to ED50and LD50values with a significant interaction with seed surface and seed width, respectively. Exposure to 5.81 mg cm−2of dried mustard powder increased common ragweed seed mortality for after-ripened seeds by 293% and by 58% for primary dormant seeds compared with untreated seeds. Mortality of common lambsquarters secondary and primary dormant seeds increased by 730% and 106%, respectively, and for wild carrot by 1,193 and 156%, respectively. Results underline the potential to incorporate biofumigation into weed management programs for repression of susceptible weed species.

Metabolite and transcript markers for the prediction of potato drought tolerance.

SummaryPotato (Solanum tuberosum L.) is one of the most important food crops worldwide. Current potato varieties are highly susceptible to drought stress. In view of global climate change, selection of cultivars with improved drought tolerance and high yield potential is of paramount importance. Drought tolerance breeding of potato is currently based on direct selection according to yield and phenotypic traits and requires multiple trials under drought conditions. Marker‐assisted selection (MAS) is cheaper, faster and reduces classification errors caused by noncontrolled environmental effects. We analysed 31 potato cultivars grown under optimal and reduced water supply in six independent field trials. Drought tolerance was determined as tuber starch yield. Leaf samples from young plants were screened for preselected transcript and nontargeted metabolite abundance using qRT‐PCR and GC‐MS profiling, respectively. Transcript marker candidates were selected from a published RNA‐Seq data set. A Random Forest machine learning approach extracted metabolite and transcript markers for drought tolerance prediction with low error rates of 6% and 9%, respectively. Moreover, by combining transcript and metabolite markers, the prediction error was reduced to 4.3%. Feature selection from Random Forest models allowed model minimization, yielding a minimal combination of only 20 metabolite and transcript markers that were successfully tested for their reproducibility in 16 independent agronomic field trials. We demonstrate that a minimum combination of transcript and metabolite markers sampled at early cultivation stages predicts potato yield stability under drought largely independent of seasonal and regional agronomic conditions.