Biology subjects

Rascher, U.

Publications and source records attributed to Rascher, U..

4 recordsLinked to original sources

bioRxiv2026

Repeatability and Heritability of UAV-Derived Canopy Traits in a Cassava Breeding Population Using Time-Series Data from Two Consecutive Growing Seasons

Cassava is a major staple crop in tropical regions, particularly in Sub-Saharan Africa, yet its productivity remains constrained by genetic and agronomic limitations. A major bottleneck in cassava breeding is the difficulty of accurately phenotyping agronomic traits under field conditions using conventional, labor-intensive methods. Here, we evaluated the potential of uncrewed aerial vehicle (UAV)-based phenotyping to quantify canopy growth traits and assess their genetic relevance under realistic field conditions. For this, multi-temporal UAV imagery was collected over two growing seasons (2018-2019 and 2019-2020) in a panel of 46 cassava genotypes planted in fields of the International Institute for Tropical Agriculture (IITA), Nigeria. Canopy height, canopy volume, and their relative growth rates (RGRh and RGRv) were extracted at the plot-level, and their seasonal dynamics and canopy-yield relationships were further assessed across developmental stages and environmental conditions. Repeatability (R) and broad-sense heritability (H2) were estimated using a linear mixed model (LMM) that partitioned genetic, genotype-by-year, and residual variance components, enabling the evaluation of both measurement reliability and genetic signal. Overall, UAV-derived growth dynamics were found to exhibit comparable patterns across genotypes, reflecting shared seasonal growth trajectories, while canopy-yield relationships varied with developmental stage and environmental conditions. In terms of genetic metrics, R was high for all UAV-derived traits (R = 0.68-0.69), indicating reliable genotype-level assessment across replicates and seasons. In contrast, H2 differed substantially among traits. Canopy volume (H2 = 0.64) and canopy height (H2 = 0.58) exhibited moderate-to-high heritability, reflecting strong genotype effects and comparatively moderate genotype-by-year interactions. However, their relative growth rates showed near-zero H2 values, driven primarily by genotype-by-year interaction, indicating a dominant environmental influence. These results demonstrate that UAV-derived canopy height and volume provide a consistent basis for genetic differentiation of cassava genotypes across environments, supporting their use in selection, whereas growth-rate traits are better suited for characterizing growth plasticity and genotype-by-environment interactions.

plant biology↗

bioRxiv2026

Robot-based 3D-multispectral monitoring of soybean in a spatially heterogenous agrivoltaic environment

Agrophotovoltaic (APV) systems provide a unique opportunity for improving agricultural land-use efficiency by combining solar energy capture via photovoltaic panels with crop production. However, in-depth information on plant growth patterns within the spatially heterogenous microclimate created by the intermittent shading of APVs is largely missing. In the present study, we implement a customized robot-mounted 3D-multispectral imaging system to closely monitor the growth and spectral reflectance patterns of a conventional soybean cultivar "Eiko" (EK) and a chlorophyll-deficient mutant variety MinnGold (MG) under an APV system. Weekly trends in canopy morphometric features revealed significant variations in plant height, 3D leaf area, light penetration, and canopy volume across the APV field depending on the proximity with the overhead solar panels for both EK and MG, with plants receiving adequate rainfall and intermittent shade performing the best. Furthermore, although spectral indices exhibited variations between EK and MG due to intrinsic differences in pigmentation, symptoms of stress could be detected for both genotypes within rain-shaded areas of the APV plot. Hence, the present investigation depicts the potential for complementary usage of robotics and machine vision for high-precision high-throughput crop monitoring under APVs, which would enable better crop management within such non-homogenous cultivation systems.

plant biology↗

bioRxiv2026

Using a planted tree biodiversity experiment to evaluate imaging spectroscopy for species classification

Field-based monitoring of tree species in forests is often sparse due to logistical constraints. Remote sensing enables repeated, spatially contiguous collection of reflectance data across large areas. Tree species classification accuracy using such data is variable, likely because most studies use observational datasets where species occurrence correlates with environmental variation. We used two sites of a tree biodiversity experiment in Germany (BIOTREE: Kaltenborn and Bechstedt), where different species have been planted with high replication under controlled diversity levels, to assess how well tree species could be classified using reflectance data from airborne imaging spectroscopy and different classification methods (linear discriminant analysis, LDA, and a non-linear support vector machine, SVM). Reflectance data for 589 wavelengths between 400-2400nm were acquired at 1m spatial resolution during peak growing season. Reflectance spectra showed large and significant variation between taxonomic classes, orders, and species, and weak, but still significant, interactions between classes or orders and diversity levels. Classification accuracy reached 100% in training datasets, 77%-83% for the four species in Kaltenborn prediction datasets, and 31%-49% for the 16 species in Bechstedt prediction datasets. LDA provided more accurate predictions than SVM; and using similarly-spaced original wavelengths with LDA was as efficient as using principal components derived from the original data. While airborne imaging spectroscopy effectively distinguished up to four tree species in our datasets, classification accuracy was lower when 16 species had to be distinguished. In these cases, the methodology may be more useful for functional diversity monitoring than for tree species classification.

ecology↗

bioRxiv2025

A non-rectifying potassium channel increases cassava drought tolerance and storage root yield

Cassava (Manihot esculenta) is an important crop for food security in the tropics, particularly for smallholder farmers in sub-Saharan Africa, where yields are often severely limited by pathogen pressure, nutrient deficiency, and water scarcity. We expressed a non-rectifying Arabidopsis thaliana potassium (K+) channel gene version, AKT2var, in the vascular tissue of cassava plants. The transgenic cassava plants had higher electron transport and CO2 assimilation rates, a higher bulk flow velocity, and increased source-sink carbohydrate transport, as demonstrated by comparative 11C-positron emission tomography and tissue-specific metabolite profiling. Cassava storage root yield was significantly increased in greenhouse experiments and in a multi-year field trial conducted under subtropical conditions. AKT2var plants were also more tolerant to drought stress and had higher storage root yield. Targeted alteration of K+ transport is therefore a promising strategy to improve cassava productivity without additional fertilizer input and in climate-adverse growing conditions.

plant biology↗