Institut für Kulturpflanzenwissenschaften

Permanent URI for this collectionhttps://hohpublica.uni-hohenheim.de/handle/123456789/12

Browse

Now showing 1 - 20 of 352

Assessing the between-country genetic correlation in maize yield using German and Polish official variety trials
(2022) Malik, Waqas Ahmed; Buntaran, Harimurti; Przystalski, Marcin; Lenartowicz, Tomasz; Piepho, Hans-Peter
Official variety testing is performed in many countries by statutory agencies in order to identify the best candidates and make decisions on the addition to the national list. Neighbouring countries can have similarities in agroecological conditions, so it is worthwhile to consider a joint analysis of data from national list trials to assess the similarity in performance of those varieties tested in both countries. Here, maize yield data from official German and Poland variety trials for cultivation and use (VCU) were analysed for the period from 1987 to 2017. Several statistical models that incorporate environmental covariates were fitted. The best fitting model was used to compute estimates of genotype main effects for each country. It is demonstrated that a model with random genotype-by-country effects can be used to borrow strength across countries. The genetic correlation between cultivars from the two countries equalled 0.89. The analysis based on agroecological zones showed high correlation between zones in the two countries. The results also showed that 22 agroecological zones in Germany can be merged into five zones, whereas the six zones in Poland had very high correlation and can be considered as a single zone for maize. The 43 common varieties which were tested in both countries performed equally in both countries. The mean performances of these common varieties in both countries were highly correlated.
Split N application and DMP based nitrification inhibitors mitigate N2O losses in a soil cropped with winter wheat
(2022) Guzman-Bustamante, Ivan; Schulz, Rudolf; Müller, Torsten; Ruser, Reiner
Nitrogen (N) fertilization to crops might lead to formation and release of reactive N—e.g. nitrate, ammonium, ammonia, nitrous oxide (N2O) —, contributing to eutrophication, atmospheric pollution, and climate change. Use of nitrification inhibitors and splitting of N fertilizer may reduce the N2O emission from arable soils cropped with winter wheat. We tested different N fertilizers treated with 3,4-dimethylpyrazol phosphate (DMPP) and 3,4-dimethylpyrazol succinic acid (DMPSA) by applying 180 kg N ha−1 in different N splitting strategies in a full annual field experiment on a loamy soil in Southwest Germany. A threefold split fertilization led to an emission of 2.3 kg N2O–N ha−1 a−1 (corresponding to a reduction of 19%) compared to a single application of ammonium sulphate nitrate (ASN) (p = 0.07). A single application rate of ASN with DMPP resulted in an emission of 1.9 kg N2O–N ha−1 a−1 and reduced N2O emissions from an ASN treatment without NI by 33%. Calcium ammonium nitrate (CAN) with DMPSA reduced N2O emissions during the vegetation period by 38% compared to CAN without a nitrification inhibitor, but this was offset by high emissions after harvest, which was driven by soil tillage with an annual reduction of 26% (CAN: 2.9 kg N2O–N ha−1 a−1; CAN + DMPSA: 2.1 kg N2O–N ha−1 a−1; p = 0.11). Among our tested treatments, a twofold split application of ASN with DMPP efficiently reduced N2O emissions and maintained grain yield when compared to the traditional system with threefold application without nitrification inhibitor. Despite resulting in lower protein contents in the twofold split application, this treatment should be further investigated as a potential compromise between wheat yield and quality optimization and climate protection.
Determination of aroma compounds in grape mash under conditions of tasting by on-line near-infrared spectroscopy
(2022) Gehlken, Jana; Pour Nikfardjam, Martin; Zörb, Christian
The production of high-quality wines requires the use of high-quality grapes. Tasting represents a widespread method for the determination of grape maturity and quality aspects such as the corresponding aroma profile. However, sensory analysis always remains subjective and it is not possible to judge only aroma compounds because the overall impression is also influenced by main components (e.g. sugars and acids). In contrast, the use of near-infrared (NIR) spectroscopy allows the simultaneous determination of various compounds without being affected by personal preferences. In this study, grape mash samples were examined under comparable conditions to those in the mouth. Differences between grape mashes with varying phytosanitary status of the corresponding grapes as well as for different grape varieties were detected. The quantified concentrations of the detected aroma compounds were used to develop calibration models for determination by NIR spectroscopy. Using global calibration models, the single aroma compounds could be determined by NIR spectroscopy with accuracies reaching from R2C = 0.365 to R2C = 0.976. Separate calibration models for cultivation region and grape colour improved the prediction accuracy. Instrumental analysis cannot totally replace sensory evaluation, however, NIR spectroscopy has the potential to be used as an objective, additional method for the evaluation of grape aroma quality.
Harvesting light: the interrelation of spectrum, plant density, secondary metabolites, and Cannabis sativa L. yield
(2024) Reichel, Philipp; Munz, Sebastian; Hartung, Jens; Graeff-Hönninger, Simone; Cocetta, Giacomo; Palmitessa, Onofrio davide
The approaching legalisation and associated increasing demand for medicinal and recreational Cannabis sativa L. will lead to a growing relevance for lighting systems designed for Cannabis sativa L. The interplay between plant density, light spectrum, light distribution, yield, and secondary metabolite distribution within the plant has not yet been studied. To fill this knowledge gap, a CBD-dominant Cannabis sativa L. strain was grown in a greenhouse experiment with two plant densities (2.66 and 12 plants −1 m −2 ) under two different light spectra. The chosen light spectra were two LED fixtures, Solray385 (SOL) and AP67, with an R: FR ratio of 12.9 and 3.7, respectively. The results indicated that light-induced effects on individual plants can be transferred to the plant stock. A low R: FR ratio induced a 16% increase in dry flower yield in the last ten days of flowering, while a change in the light spectrum could increase the potential maximum plant density per square metre. The two spectra did not affect (CBD + CBDA) yield, as a lower flower yield compensated for a higher concentration. CBDA concentration was not significantly affected by plant density. In contrast, the higher density led to an increased total cannabidiol concentration (CBD + CBDA) and altered the distribution of terpenes. Here, the light distribution over the plant stock is particularly decisive, as a more homogenous illumination led to an increased terpene concentration of up to 41%. A Photon Conversion Efficacy (PCE) of 0.05 g mol −1 under SOL and 0.06 g mol −1 under AP67 was achieved. Plants in the centre under the highest light intensity of 1200 PAR showed up to 48% reduced efficacy. These results strongly suggest that light intensity needs to be fine-tuned to the cultivation system to prevent a reduction in efficacy, resulting in yield and quality losses.
Protein use efficiency and stability of baking quality in winter wheat based on the relation of loaf volume and grain protein content
(2022) Laidig, Friedrich; Hüsken, Alexandra; Rentel, Dirk; Piepho, Hans-Peter
The most important trait for baking quality of winter wheat is loaf volume (V). It is mostly determined by grain protein content (GPC) and quality. New varieties with a high potential of grain protein use efficiency (ProtUE) are very important for reducing the surplus use of nitrogen fertilizer in areas where nitrogen leaching is large. This is also an important goal of agricultural policies in the European Union. Additionally, ProtUE needs to be very stable across environments in the face of progressing climate change with more volatile growing conditions. We evaluated a new approach to assess ProtUE and stability based on the V–GPC relationship instead of using only single traits. The study comprised 11,775 baking tests from 355 varieties grown 1988–2019 in 668 different environments in Germany. V was predicted by quadratic and linear regression functions for quality groups, indicating a reduction of ProtUE from 1988 to 2019. We introduced a dynamic and a static approach to assess ProtUE and stability as potential criteria in variety registration. We found a considerably lower heritability of the dynamic ProtUE ( h 2 = 43%) compared to the static ProtUE ( h 2 = 92%) and a lower dynamic stability ( h 2 = 32%) than for the static stability ( h 2 = 51%). None of these measures is in conflict with the selection for high V. In particular, V and static ProtUE are strongly genetically associated ( r = 0.81), indicating an advantage of the static over the dynamic approach.
Digestate composition affecting N fertiliser value and C mineralisation
(2022) Häfner, Franziska; Hartung, Jens; Möller, Kurt
A variety of organic feedstocks can be used for anaerobic digestion, resulting in digestates with different compositions, affecting the fertiliser value. Therefore, two experiments were conducted to assess (1) differences in the nitrogen (N) fertiliser value of seven digestates from different feedstocks in a 2-year field experiment with spring wheat, and (2) the degradability of organic matter (OM) in the digestates within an aerobic incubation experiment. In the field, mineral fertiliser equivalents were in a range of 18–60% (1st year) and 39–83% (2nd year). Fertiliser properties could describe 58.9–74.2% of the N offtake variance among digestates. In the incubation experiment, digestates produced 720–1900 mg CO2-C kg−1. After 56 days, 61% of organic C added by food waste digestate has been mineralised, compared to 16–22% for the other digestates. Digestate composition (C/N, Corg/Norg, carbonate, cellulose, lignin, and crude fibre) could explain 90.4% of the CO2 evolution. In both experiments, digested food waste stood out among digestates with the highest N offtake and highest OM mineralisation. In conclusion, differences in fertiliser value and OM degradability could be related to compositional variations. However, apart from food waste, the composition had only minor influence on digestate performance after soil application.Graphical Abstract
Bioenergy potential of Europe's perennial and biennial wildflowers: a combustion performance benchmark
(2025) von Cossel, Moritz; Hieber, Caroline; Iqbal, Yasir; Berwanger, Eva; Lebendig, Florian; Müller, Michael; Jablonowski, Nicolai David; von Cossel, Moritz; Biobased Resources in the Bioeconomy (340b), University of Hohenheim, Stuttgart, Germany; Hieber, Caroline; Biobased Resources in the Bioeconomy (340b), University of Hohenheim, Stuttgart, Germany; Iqbal, Yasir; College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha, China; Berwanger, Eva; Biobased Resources in the Bioeconomy (340b), University of Hohenheim, Stuttgart, Germany; Lebendig, Florian; Institute of Energy Materials and Devices, IMD‐1: Structure and Function of Materials, Forschungszentrum Jülich GmbH, Jülich, Germany; Müller, Michael; Institute of Energy Materials and Devices, IMD‐1: Structure and Function of Materials, Forschungszentrum Jülich GmbH, Jülich, Germany; Jablonowski, Nicolai David; Institute of Bio‐ and Geosciences, IBG‐2: Plant Sciences, Forschungszentrum Jülich GmbH, Jülich, Germany
The European Commission prioritizes addressing environmental issues like agrobiodiversity loss within a thriving bioeconomy's defossilization. This study investigates eight native European herbaceous flowering wild plant species (WPS) like common tansy (Tanacetum vulgare L.) and wild teasel (Dipsacus fullonum L.) as co‐substrates for pellet combustion, aiming for more biodiversity‐friendly bioenergy cropping systems. A long‐term field trial in southwest Germany examined dry matter (DM) yield and biochemical composition's influence on combustion properties for these WPS and two common bioenergy crops, Miscanthus (Miscanthus x giganteus Greef et Deuter) and Sida (Sida hermaphrodita L. var. Rusby), over two growing seasons. All eight WPS showed suitable combustion properties, comparable to Sida, with significantly higher ash melting temperatures than Miscanthus. This is largely attributed to elevated calcium (5.6–15.3 mg g−1 DM) and magnesium (0.6–2.4 mg g−1 DM) contents. A consistent WPS biomass composition is suggested by no significant year effect. Additionally, lower SO2 and HCl fugacity indicated more environmentally friendly combustion than Miscanthus. However, only a few WPS matched Miscanthus's high DM yield (6.0–12.3 Mg ha−1). This underscores the need for broader WPS investigation to find effective combined solutions for bioenergy and rural environmental protection.
Guard cell‐specific metabolic responses to drought stress in maize
(2025) Lehr, Patrick Pascal; Erban, Alexander; Hartwig, Roman Paul; Wimmer, Monika Andrea; Kopka, Joachim; Zörb, Christian
Understanding crop responses to drought stress is crucial for securing future agricultural productivity. Guard cells regulate transpiration and thus the yield burden under drought conditions. Therefore, the influence of repeated drought stress on the guard cell metabolome of Zea mays L. was investigated to improve our understanding of crop resilience mechanisms. A controlled greenhouse experiment with physiological evaluation and a non‐targeted metabolomics approach was used to analyse unprimed and primed guard cells. Primed and unprimed maize plants showed similar overall physiological and metabolic responses to drought, with gas exchange and general metabolic patterns largely unaffected by priming. However, distinct priming effects emerged in specific metabolites. Metabolites of the alanine and aspartate pathway, as well as those of the glycine, serine and threonine pathway were less impacted by drought stress in guard cells than in mesophyll cells, suggesting the emphasis of plants to maintain stable guard cell metabolomes for functional integrity. In contrast, the increase in sugar concentrations in guard cells was similar to that in mesophyll cells, suggesting a pivotal role of sugars in guard cells during drought conditions. New insights into cell type‐specific metabolic responses to drought stress will contribute to a better understanding of stress memory in maize. Enhancing guard cell resilience could help optimise water use efficiency for sustainable agricultural production under climate change conditions.
Evaluation of crop model-based simplified marginal net return maximising nitrogen application rates on site-specific level in maize
(2024) Memic, E.; Trenz, J.; Heshmati, S.; Graeff, S.
Crop growth models such as DSSAT-CERES-Maize have proven to be useful for analysing plant growth and yield within homogenous land units. The paper presents results of newly developed model-based site-specific Soil Profile Optimisation (SPO) tools in combination with an updated version of an already published Nitrogen Prescription Model (NPM). Site-specific soil profiles were generated through an inverse modelling approach based on measured site-specific yield (point-based) and tops weight (above-ground biomass time-series) and evaluated. Site-specific soil profiles generated based only on measured yield variability were able to explain 72% (R 2 0.72) of yield variability (dependent variable) based on selected soil profile input parameters (independent variable). Site-specific soil profiles generated based on measured yield and tops variability simultaneously (multiple target variable) explained 68% of yield variability (R 2 0.68). The NPM uses the SPO generated site-specific soil profiles for economic evaluation of site-specific N application rates. NPM simulated N application rates, aiming at the maximisation of marginal net return (MNR) were 25% lower compared to the uniform N application rates with an assumed grain and N price of 0.17 and 0.3 Euro kg −1 respectively, under rainfed conditions over three years based on soil profiles generated via an inverse modelling approach only from measured yield variability (one target variable). N application rates were 28% lower when based on soil profiles generated from simultaneously included grain and tops variability in the inverse modelling approach. The results highlight the importance of site-specific fertilizer management when maximising MNR.
Comparative analysis of minerals, carotenoids, and tocochromanols in ripe seeds, immature seeds and tepals of bitter and non-bitter quinoa genotypes
(2026) Lauer, Luise Amelie; Kollmar, Marius; Schmöckel, Sandra M.; Frank, Jan
Quinoa (Chenopodium quinoa Willd.) contains high amounts of minerals, carotenoids and vitamin E (tocochromanols), but also antinutrients, such as saponins. Based on their saponin content, quinoa can be classified into “bitter” and “non-bitter” genotypes. Carotenoids (lutein, zeaxanthin, β-carotene, β-cryptoxanthin), vitamin E and saponins share a precursor in their respective biosynthesis pathways. Thus, we investigated whether the downregulation of saponin biosynthesis in non-bitter quinoa affects the contents of minerals and lipid-soluble compounds compared to bitter quinoa. The analytes were quantified in ripe seeds of fifty (23 bitter and 27 non-bitter) quinoa genotypes. A subset of twelve genotypes (6 bitter and 6 non-bitter) was analyzed for carotenoids and tocochromanols in immature seeds and their tepals. Total mineral (8206 mg/kg vs. 8646 mg/kg) and carotenoid contents (314 µg/100 g vs. 242 µg/100 g) did not differ between bitter and non-bitter ripe seeds. However, non-bitter quinoa seeds contained higher lutein and total tocochromanol (driven by tocotrienols) contents than bitter genotypes. Carotenoid and tocochromanol contents in immature seeds and tepals did not differ between phenotypes; tepals had up to 74-fold higher contents than the seeds. In conclusion, the downregulation of saponin biosynthesis in non-bitter quinoa genotypes does not affect the biosynthesis of carotenoids and tocochromanols.
Generic optimization approach of soil hydraulic parameters for site-specific model applications
(2024) Trenz, Jonas; Memic, Emir; Batchelor, William D.; Graeff-Hönninger, Simone
Site-specific crop management is based on the postulate of varying soil and crop requirements in a field. Therefore, a field is separated into homogenous management zones, using available data to adapt management practices environment to maximize productivity and profitability while reducing environmental impacts. Due to advancing sensor technologies, crop growth and yield data on more minor scales are common, but soil data often needs to be more appropriate. Crop growth models have shown promise as a decision support tool for site-specific farming. The Decision Support System for Agrotechnology Transfer (DSSAT) is a widely used point-based model. To overcome the problem of inappropriate soil input data problem, this study introduces an external plug-in program called Soil Profile Optimizer (SPO), which uses the current DSSAT v4.8 to calibrate soil profile parameters on a site-specific level. Developed as an inverse modelling approach, the SPO can calibrate selected soil profile parameters by targeting available in-season plant data. Root Mean Square Error (RMSE) and normalized RMSE as error minimization criteria are used. The SPO was tested and evaluated by comparing different simulation scenarios in a case study of a 3-yr field trial with maize. The scenario with optimized soil profiles, conducted with the SPO, resulted in an R 2 of 0.76 between simulated and observed yield and led to significant improvements compared to the scenario conducted with field scale soil profile information (R 2 0.03). The SPO showed promise in using spatial plant measurements to estimate management zone scale soil parameters required for the DSSAT model.
Editorial: Agroecological practices to enhance resilience of farming systems
(2025) Scordia, Danilo; von Cossel, Moritz; Gresta, Fabio
Regulation of heterosis-associated gene expression complementation in maize hybrids
(2025) Pitz, Marion; Baldauf, Jutta A.; Piepho, Hans-Peter; Yu, Peng; Schoof, Heiko; Mason, Annaliese S.; Li, Guoliang; Hochholdinger, Frank
Background: Classical genetic concepts to explain heterosis attribute the superiority of F1-hybrids over their homozygous parents to the complementation of unfavorable by beneficial alleles (dominance) or to heterozygote advantage (overdominance). Here we analyze 112 intermated B73xMo17 recombinant inbred lines of maize and their backcrosses to their original parents B73 and Mo17 to obtain hybrids with an average heterozygosity of ~ 50%. This genetic architecture allows studying the influence of homozygous and heterozygous genomic regions on gene expression in hybrids. Results: We demonstrate that single parent expression (SPE) complementation explains between − 8% and 29% of the mid-parent heterotic variance in these hybrids. In this expression pattern, consistent with dominance, genes are active in only one parent and in the hybrid, thus increasing the number of expressed genes in hybrids. Furthermore, we establish that eQTL regulating SPE genes are predominantly located in heterozygous regions of the genome. Finally, we identify an SPE gene that regulates lateral root density in hybrids. Remarkably, the activity of this gene depends on the presence of a Mo17 allele in an eQTL that regulates this gene. Conclusions: Here we show that dominance of SPE genes influences the number of active genes in hybrids, while heterozygosity is instrumental for the regulation of these genes. This finding supports the notion that the genetic constitution of distant regulatory elements is instrumental for the activity of heterosis-associated genes. In summary, our results connect genetic variation at regulatory loci and the degree of heterozygosity with phenotypic variation of heterosis via SPE complementation.
Buckwheat in Germany: The effect of variety and sowing date on agronomic traits
(2025) Grimes, Samantha J.; Afzal, Muhammad; Tako, Rea; Hahn, Volker; Graeff‐Hönninger, Simone; Longin, C. Friedrich H.; Grimes, Samantha J.; Department of Agronomy, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany; Afzal, Muhammad; State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany; Tako, Rea; State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany; Hahn, Volker; State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany; Graeff‐Hönninger, Simone; Department of Agronomy, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany; Longin, C. Friedrich H.; State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany
Buckwheat (Fagopyrum esculentum Moench) requires minimal agrochemical inputs and delivers grains with a high nutritional profile—the perfect prerequisites for future sustainable farming. However, it is currently consumed and produced in only a few countries. The aim of this study was to investigate the potential to successfully grow buckwheat in Germany and to elaborate first insights for local breeding. Therefore, a total of 33 buckwheat varieties were tested across three locations, 3 years, and two different sowing dates. The average yield was 2.3 t ha −1 , ranging from 1.4 to 3.1 t ha −1 across varieties. Similar yields were observed for both early and late sowing dates, and across all tested varieties. All but two of the very late‐maturing common buckwheat varieties could be safely harvested in all locations also on the late sowing date. Key prerequisites to establish local breeding were met, including large genetic variation and high heritability for important agronomic traits. In summary, this study highlights the importance of variety selection and targeted breeding focusing on early‐maturing buckwheat varieties, paving the way for potential double‐cropping systems in Germany that use buckwheat as a second crop and significantly enhance its profitability for farmers.
Impact of soil improvers on soil health: A data mining approach to support sustainable agriculture across the EU
(2025) Nolfi, Lorenzo; Bindo, Arianna; Di Gregorio, Luciana; Costanzo, Manuela; Caldara, Marina; Tabacchioni, Silvia; Visca, Andrea; Salo, Tapio; Bauerle, Andrea; Hansen, Veronika; Bernini, Roberta; Varese, Giovanna Cristina; Manikas, Ioannis; Marmiroli, Nelson; Palojärvi, Ansa; Bevivino, Annamaria
Soil health is crucial for the sustainability of agricultural practices and ecosystem resilience. Using a data mining approach, this study aims to explore emerging themes related to the impact of soil improvers on soil health by analyzing results from various EU-funded agricultural projects, with the final goal of identifying the key factors driving the effectiveness of soil amendments. By integrating data mining and text analysis, the study extracts, aggregates, and visualizes insights, providing a comprehensive overview of innovative strategies to enhance soil fertility and promote ecological balance. This integrated analytical framework offers a nuanced understanding of the conceptual landscape surrounding soil health in EU projects, highlighting the multifaceted roles of organic amendments and microbial solutions. Our findings underscore the critical link between organic amendments and soil health, highlighting their potential as strategic tools for achieving more sustainable agricultural systems. These findings provide a basis for refining soil management strategies in agriculture and support the development of evidence-based policies aimed at improving soil health and fostering ecological balance across Europe.
Regression approaches for modeling genotype-environment interaction and making predictions into unseen environments
(2026) Hrachov, Maksym; Piepho, Hans-Peter; Rahman, Niaz Md. Farhat; Malik, Waqas Ahmed; Hrachov, Maksym; Biostatistics Unit, Institute of Crop Science, University of Hohenheim, 70593, Stuttgart, Germany; Piepho, Hans-Peter; Biostatistics Unit, Institute of Crop Science, University of Hohenheim, 70593, Stuttgart, Germany; Rahman, Niaz Md. Farhat; Bangladesh Rice Research Institute (BRRI), Gazipur, Bangladesh; Malik, Waqas Ahmed; Biostatistics Unit, Institute of Crop Science, University of Hohenheim, 70593, Stuttgart, Germany
In plant breeding and variety testing, there is an increasing interest in making use of environmental information to enhance predictions for new environments. Here, we will review linear mixed models that have been proposed for this purpose. The emphasis will be on predictions and on methods to assess the uncertainty of predictions for new environments. Our point of departure is straight-line regression, which may be extended to multiple environmental covariates and genotype-specific responses. When observable environmental covariates are used, this is also known as factorial regression. Early work along these lines can be traced back to Stringfield & Salter (1934) and Yates & Cochran (1938), who proposed a method nowadays best known as Finlay-Wilkinson regression. This method, in turn, has close ties with regression on latent environmental covariates and factor-analytic variance-covariance structures for genotype-environment interaction. Extensions of these approaches – reduced rank regression, kernel- or kinship-based approaches, random coefficient regression, and extended Finlay-Wilkinson regression – will be the focus of this paper. Our objective is to demonstrate how seemingly disparate methods are very closely linked and fall within a common model-based prediction framework. The framework considers environments as random throughout, with genotypes also modeled as random in most cases. We will discuss options for assessing uncertainty of predictions, including cross validation and model-based estimates of uncertainty, the latter one being estimated using our new suggested approach. The methods are illustrated using a long-term rice variety trial dataset from Bangladesh.
Spotlight on agroecological cropping practices to improve the resilience of farming systems: a qualitative review of meta-analytic studies
(2025) von Cossel, Moritz; Scordia, Danilo; Altieri, Miguel; Gresta, Fabio
The capacity of agriculture to withstand or recover from increasing stresses (i.e., resilience) will be continuously challenged by extreme climate change events in the coming decades, altering the growing conditions for crop species. By prioritizing natural processes, agroecology seeks to foster climate change adaptation, boost resilience, and contribute to a low-emission agricultural system. Nineteen different agroecological practices using resilience-related terms and “meta-analysis”, within the subject areas ‘Agriculture and Biological Science’ and ‘Environmental Science’ were addressed, and 34 meta-analyses were reviewed to summarize the state-of-the-art agroecological adaptative strategies applied globally, and the current knowledge gaps on the role of agroecological practices in improving farming system resilience. Two main agroecological strategies stand out: i) crop diversification and ii) ecological soil management. The most frequent diversification practices included agroforestry, intercropping, cover cropping, crop rotation, mixed cropping, mixed farming, and the use of local varieties. Soil management practices included green manure, no-till farming, mulching, and the addition of organic matter. The analyzed studies highlight the complex interplay among soil, plant, climate, management, and socio-economic contexts within the selected agroecological practices. The results varied—positive, null, or negative—depending largely on site-specific factors. Developing and understanding more complex systems in a holistic approach, that integrates plants and animals across multiple trophic levels (feeding relationships, nutrient cycling, and aligning with the principles of a circular economy) is essential. More research is, therefore, needed to understand the interactions between crop diversity and soil management, their impacts on resilience, and how to translate research into practical strategies that farmers can implement effectively.
Expanding the BonnMu sequence‐indexed repository of transposon induced maize (Zea mays L.) mutations in dent and flint germplasm
(2024) Win, Yan Naing; Stöcker, Tyll; Du, Xuelian; Brox, Alexa; Pitz, Marion; Klaus, Alina; Piepho, Hans‐Peter; Schoof, Heiko; Hochholdinger, Frank; Marcon, Caroline
The BonnMu resource is a transposon tagged mutant collection designed for functional genomics studies in maize. To expand this resource, we crossed an active Mutator (Mu) stock with dent (B73, Co125) and flint (DK105, EP1, and F7) germplasm, resulting in the generation of 8064 mutagenized BonnMu F2‐families. Sequencing of these Mu‐tagged families revealed 425 924 presumptive heritable Mu insertions affecting 36 612 (83%) of the 44 303 high‐confidence gene models of maize (B73v5). On average, we observed 12 Mu insertions per gene (425 924 total insertions/36 612 affected genes) and 53 insertions per BonnMu F2‐family (425 924 total insertions/8064 families). Mu insertions and photos of seedling phenotypes from segregating BonnMu F2‐families can be accessed through the Maize Genetics and Genomics Database (MaizeGDB). Downstream examination via the automated Mutant‐seq Workflow Utility (MuWU) identified 94% of the presumptive germinal insertion sites in genic regions and only a small fraction of 6% inserting in non‐coding intergenic sequences of the genome. Consistently, Mu insertions aligned with gene‐dense chromosomal arms. In total, 42% of all BonnMu insertions were located in the 5′ untranslated region of genes, corresponding to accessible chromatin. Furthermore, for 38% of the insertions (163 843 of 425 924 total insertions) Mu1, Mu8 and MuDR were confirmed to be the causal Mu elements. Our publicly accessible European BonnMu resource has archived insertions covering two major germplasm groups, thus facilitating both forward and reverse genetics studies.
Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data
(2024) Lourenço, Vanda M.; Ogutu, Joseph O.; Rodrigues, Rui A.P.; Posekany, Alexandra; Piepho, Hans-Peter
Background: The accurate prediction of genomic breeding values is central to genomic selection in both plant and animal breeding studies. Genomic prediction involves the use of thousands of molecular markers spanning the entire genome and therefore requires methods able to efficiently handle high dimensional data. Not surprisingly, machine learning methods are becoming widely advocated for and used in genomic prediction studies. These methods encompass different groups of supervised and unsupervised learning methods. Although several studies have compared the predictive performances of individual methods, studies comparing the predictive performance of different groups of methods are rare. However, such studies are crucial for identifying (i) groups of methods with superior genomic predictive performance and assessing (ii) the merits and demerits of such groups of methods relative to each other and to the established classical methods. Here, we comparatively evaluate the genomic predictive performance and informally assess the computational cost of several groups of supervised machine learning methods, specifically, regularized regression methods, deep , ensemble and instance-based learning algorithms, using one simulated animal breeding dataset and three empirical maize breeding datasets obtained from a commercial breeding program. Results: Our results show that the relative predictive performance and computational expense of the groups of machine learning methods depend upon both the data and target traits and that for classical regularized methods, increasing model complexity can incur huge computational costs but does not necessarily always improve predictive accuracy. Thus, despite their greater complexity and computational burden, neither the adaptive nor the group regularized methods clearly improved upon the results of their simple regularized counterparts. This rules out selection of one procedure among machine learning methods for routine use in genomic prediction. The results also show that, because of their competitive predictive performance, computational efficiency, simplicity and therefore relatively few tuning parameters, the classical linear mixed model and regularized regression methods are likely to remain strong contenders for genomic prediction. Conclusions: The dependence of predictive performance and computational burden on target datasets and traits call for increasing investments in enhancing the computational efficiency of machine learning algorithms and computing resources.
Correction to: Assessing the efficiency and heritability of blocked tree breeding trials
(2024) Piepho, Hans-Peter; Williams, Emlyn; Prus, Maryna

Browse

Recent Submissions