Achim Walter

Organic fertilizers often contribute to nitrate (NO ₃ ⁻ ) leaching and impair drinking water quality, because the mineralization of applied organic nitrogen (N) is often asynchronous with crop N uptake. Crop yield, crop N uptake, seepage water and NO ₃ ⁻ leaching were investigated in a lysimeter facility (Zurich, Switzerland) under temperate climate conditions during two consecutive four-year organically managed crop rotations. The study compared untreated cattle slurry, anaerobically digested slurry, liquid digestate from a non-agricultural biogas plant and a zero-N fertilization control. The ammonium N (NH ₄ -N) fraction of the organic fertilizers was labeled with ¹⁵ N tracer to investigate the fate of NH ₄ -N in the plant-soil–water system. Organic fertilization significantly increased crop yields and N uptake, with no differences among organic fertilizers, despite the higher NH ₄ -N proportion of the digestates. Mean annual NO ₃ ⁻ leaching was low (12–18 kg N ha ⁻¹ ) and was not increased by organic fertilization, indicating that N inputs were balanced by crop N uptake. Digestates did not differ from untreated cattle slurry in NO ₃ ⁻ leaching. Higher modeled ammonia (NH ₃ ) emissions of digestates suggest that N losses may occur primarily via NH ₃ volatilization rather than NO ₃ ⁻ leaching. The ¹⁵ N recovery showed ~1% of the applied NH ₄ -N was lost through NO ₃ ⁻ leaching, whereas 23–31% was recovered in crops, and 18–23% remained in the topsoil. These results highlight the importance of crop N uptake and the role of the soil as a long-term reservoir for fertilizer-derived N, with direct NO ₃ ⁻ leaching from the NH ₄ -N fraction of organic fertilizers playing only a minor role.

Climate-resilient crop varieties are exceedingly important for global food security. High-accuracy predictions of crop phenology in future climate scenarios require a more precise description of putative crop-environment interactions. While the accuracy of these predictions is affected by three primary sources of uncertainty — the phenology model, the climate scenario data, and their interaction — commonly used approaches consider only the phenology model as a source of uncertainty, while uncertainties arising from climate scenario data and their interaction with the model are commonly neglected. To address this gap, we developed a novel dynamic crop phenology model framework that simultaneously integrates uncertainties arising from all three sources. This model framework was developed using a large dataset of winter wheat phenology ratings and environmental covariate observations, encompassing more than 2500 locations spanning more than 80 years. It incorporates and combines up to seven environmental covariates in an additive manner, resulting in a large set of possible models from which to select. For each model, the phenological development follows a defined response curve per environmental covariate. Using climate scenario projections in Switzerland, our proposed effective model selection process helps to find the best model that reduces uncertainties in predicting future crop phenology. In addition to predictions, the model can pinpoint the driving environmental covariates for different phenological phases. Four major phenological stages are predicted for an independent subset of the large dataset and future climate scenario projections. Predictions for future climate scenario projections indicate a 22-day earlier heading for 2070–2099 compared to the reference period (1981–2010). Ignoring uncertainties arising from climate data results in contradictory predictions. These findings underscore the importance of considering all three uncertainty sources — phenology model, climate scenario data, and their interaction — in accurately predicting phenology under future climates.

Soybean growth is determined by the interaction of genetic, environmental, and management factors. In the context of future climate and climate extremes, understanding genotype by environment interaction (GxE) will be crucial for selecting resilient breeding lines and optimizing management practices to minimize stress. This requires an in depth elucidation of stressful weather conditions and differing temporal responses of genotypes to those conditions. In field studies, however, the environment is often treated as a static factor, and the specific effects of weather variability on crop growth remain poorly understood. Here, we present a longitudinal dataset comprising 17,247 high-resolution RGB images of soybean breeding lines collected throughout eight years in Eschikon, Switzerland. Top-of-canopy images were acquired throughout the entire growing seasons and complemented by hourly weather data, enabling a comprehensive analysis of soybean growth dynamics under varying field conditions. High spatio-temporal image resolution allows detailed analysis of growth dynamics and GxE, supporting identification of stress-tolerant genotypes to improve yield prediction and yield stability.

Green canopy cover dynamics extracted from high-throughput RGB imagery via automated labeled segmentation enabled nonlinear modeling that accounted for short-term weather variation, explaining up to 56% of protein yield variation in 150 soybean genotypes across ten seasons and enabling prediction in new environments as well as identification of tolerant genotypes.

Bees that nest in the soil in self‐excavated burrows comprise the majority of wild bee species and provide important pollination and soil functions, yet many species are threatened. Conservation efforts for ground‐nesting bees are often hindered by limited knowledge of their nesting habitat requirements, in part because nests are difficult to locate and efficient methods for monitoring nesting sites are lacking. Automated detection and monitoring of soil mounds (tumuli) produced by ground‐nesting bees, indicating nest presence, could provide new insights into bee nesting biology and population dynamics while also providing crucial data to support conservation and management. Image‐based methods, such as the automated acquisition of high‐resolution aerial imagery using drones combined with modern computer vision techniques, offer a promising path toward scalable systems for detecting and monitoring bee nest tumuli across large areas. Here, we evaluate the feasibility of integrating drone‐based image acquisition with deep learning to detect tumuli representing bee nests and to distinguish them from other soil surface deposits, such as earthworm casts. We demonstrate this approach on a 120 m ² area of a densely populated nesting aggregation of Lasioglossum malachurum on bare soil containing numerous earthworm casts. Our model reliably detected bee nest tumuli, achieving an F ₁ score of 0.90 (precision: 0.89, recall: 0.91). Misclassifications mainly arose from atypically shaped tumuli (e.g., new and incomplete or damaged), and from cases where tumuli overlapped, but no earthworm casts were confused for bee nest tumuli. This pilot study represents a step toward more efficient monitoring of ground‐nesting bees and demonstrates the potential of this approach under specific conditions. Future work could evaluate its applicability across additional habitats and species and explore alternative methods, such as image segmentation, which may be better suited for cases with less distinct tumuli or extensive overlap among soil mounds.

Photosynthesis supplies energy not only for plant biomass production but also for symbiotic processes such as nitrogen (N) fixation. Whereas the potential for further genetic gains in productivity of major crops from improved light interception and harvest index has largely been exhausted, naturally occurring or induced genetic variation in photosynthetic traits still offers considerable potential for further yield improvement. However, since photosynthesis is highly dynamic under fluctuating field conditions, it is difficult to conduct a targeted selection for photosynthetic performance unless high spatial and temporal resolution data are available. To bridge this gap, we installed a light-induced fluorescence transient (LIFT) device on an autonomous field robot to measure the quantum efficiency of photosystem II (Fq'/Fm'), which has been shown to be well correlated with overall photosynthetic performance. The LIFT method uses sub-saturating flashes at a fast repetition rate to induce maximum fluorescence, enabling measurements in less than 1 ms from a distance of up to 1 m. The robot moves at a speed of 0.5 m s-1, autonomously navigating the entire field based on global navigation satellite system (GNSS) coordinates. Spectral measurements and stereo red, green, and blue (RGB) cameras provide additional information about three-dimensional (3D) plant architecture-related traits, such as leaf angle and light intensity on the target leaf. The resulting high spatiotemporal resolution maps of photosynthetic efficiency provide detailed information about the growth performance of plants in agronomic field trials or plant breeding nurseries.

Photosynthesis supplies energy not only for plant biomass production but also for symbiotic processes such as nitrogen (N) fixation. Whereas the potential for further genetic gains in productivity of major crops from improved light interception and harvest index has largely been exhausted, naturally occurring or induced genetic variation in photosynthetic traits still offers considerable potential for further yield improvement. However, since photosynthesis is highly dynamic under fluctuating field conditions, it is difficult to conduct a targeted selection for photosynthetic performance unless high spatial and temporal resolution data are available. To bridge this gap, we installed a light-induced fluorescence transient (LIFT) device on an autonomous field robot to measure the quantum efficiency of photosystem II (Fq'/Fm'), which has been shown to be well correlated with overall photosynthetic performance. The LIFT method uses sub-saturating flashes at a fast repetition rate to induce maximum fluorescence, enabling measurements in less than 1 ms from a distance of up to 1 m. The robot moves at a speed of 0.5 m s-1, autonomously navigating the entire field based on global navigation satellite system (GNSS) coordinates. Spectral measurements and stereo red, green, and blue (RGB) cameras provide additional information about three-dimensional (3D) plant architecture-related traits, such as leaf angle and light intensity on the target leaf. The resulting high spatiotemporal resolution maps of photosynthetic efficiency provide detailed information about the growth performance of plants in agronomic field trials or plant breeding nurseries.

In variety testing and breeding of wheat ( Triticum aestivum L.), it is crucial to know the timing of phenological stages and the senescence behavior of genotypes to select for locally adapted varieties. Sound knowledge of the timing of phenological stages also allows for a more meaningful interpretation of measurements such as yield, quality, or disease ratings. In the presence of stresses, only a combined characterization of phenology and environmental conditions can allow for insights into unraveling stress resistance and stress avoidance. Capturing these traits visually in the field is very time‐consuming. Here, a semimobile PhenoCam setup was used to track phenology and senescence from ear emergence to full maturity. PhenoCams mounted on field masts took images of wheat plot trials on a daily basis. In a partial least squares regression analysis, the temporal features of multiple vegetation indices were combined in one model to track phenology and senescence. The method was compared with visual reference methods and repeated drone flights with a multispectral camera. The Pearson's correlation between visual reference methods and PhenoCam predictions was stronger than 0.8, often above 0.9, for most stages. An economic analysis showed that PhenoCams are economically interesting, especially for observing remote experimental sites. Thus, PhenoCams offer a cost‐effective replacement for visual ratings of phenology and senescence, particularly in the context of multienvironment trials.

Selecting soybean lines that thrive under increasingly extreme weather conditions requires a deep understanding of genotype by environmental interaction (GxE) interactions. While controlled experiments offer valuable insights, they rarely capture the complexity of real field conditions. Here, we combine high-resolution, 8-year field imagery with weather data to model soybean canopy growth using automatically labeled deep learning for soybean-weed segmentation and one-stage nonlinear mixed-effects modeling. The models revealed distinct genotypic growth patterns, allowing to pinpoint environmental covariates, specifically photothermal product and precipitation, as drivers of canopy development. Two breeding lines, ‘Everest’ and ‘Kalinka’ recently released as varieties, show stable performance across contrasting weather scenarios with minimal GxE interaction. Genotypic model coefficients predicted key agronomic traits such as maturity timing and protein yield. This demonstrates that growth models can reveal genotypic resilience to environmental variation, even from single-environment measurements, offering valuable insights for breeding and climate adaptation.

Soybean growth is determined by the interaction of genetic, environmental, and management factors. In the context of future climate and climate extremes, understanding genotype by environment interaction (GxE) will be crucial for selecting resilient breeding lines and optimizing management practices to minimize stress. As stress periods occur periodically in a season, in depth knowledge, about causing weather variables and differing responses of genotypes over time is required. In field studies, however, the environment is often treated as a static factor, and the specific effects of weather variability on growth remain poorly understood. Here, we present a longitudinal dataset comprising 17,247 high-resolution RGB images of soybean breeding line collected over eight years in Eschikon, Switzerland. Top of canopy images were acquired throughout the entire growing seasons and complemented by hourly weather data, enabling a comprehensive analysis of soybean growth dynamics under varying field conditions. High spatio-temporal image resolution enables detailed analysis of growth dynamics and GxE, supporting identification of stress-tolerant genotypes to improve yield prediction and yield stability.