Machine Learning, Compositional and Fractal Models to Diagnose Soil Quality and Plant Nutrition

Soils, nutrients and other factors support human food production. The loss of high-quality soils and readily minable nutrient sources pose a great challenge to present-day agriculture. A comprehensive scheme is required to make wise decisions on system’s sustainability and minimize the risk of crop failure. Soil quality provides useful indicators of its chemical, physical and biological status. Tools of precision agriculture and high-throughput technologies allow acquiring numerous soil and plant data at affordable costs in the perspective of customizing recommendations. Large and diversified datasets must be acquired uniformly among stakeholders to diagnose soil quality and plant nutrition at local scale, compare side-by-side defective and successful cases, implement trustful practices and reach high resource-use efficiency. Machine learning methods can combine numerous edaphic, managerial and climatic yield-impacting factors to conduct nutrient diagnosis and manage nutrients at local scale where factors interact. Compositional data analysis are tools to run numerical analyses on interacting components. Fractal models can describe aggregate stability tied to soil conservation practices and return site-specific indicators for decomposition rates of organic matter in relation to soil tillage and management. This chapter reports on machine learning, compositional and fractal models to support wise decisions on crop fertilization and soil conservation practices

Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations

We introduce a novel modeling approach for time series imputation and forecasting, tailored to address the challenges often encountered in real-world data, such as irregular samples, missing data, or unaligned measurements from multiple sensors. Our method relies on a continuous-time-dependent model of the series' evolution dynamics. It leverages adaptations of conditional, implicit neural representations for sequential data. A modulation mechanism, driven by a meta-learning algorithm, allows adaptation to unseen samples and extrapolation beyond observed time-windows for long-term predictions. The model provides a highly flexible and unified framework for imputation and forecasting tasks across a wide range of challenging scenarios. It achieves state-of-the-art performance on classical benchmarks and outperforms alternative time-continuous models

Conditioning machine learning models to adjust lowbush blueberry crop management to the local agroecosystem

Agroecosystem conditions limit the productivity of lowbush blueberry. Our objectives were to investigate the effects on berry yield of agroecosystem and crop management variables, then to develop a recommendation system to adjust nutrient and soil management of lowbush blueberry to given local meteorological conditions. We collected 1504 observations from N-P-K fertilizer trials conducted in Quebec, Canada. The data set, that comprised soil, tissue, and meteorological data, was processed by Bayesian mixed models, machine learning, compositional data analysis, and Markov chains. Our investigative statistical models showed that meteorological indices had the greatest impact on yield. High mean temperature at flower bud opening and after fruit maturation, and total precipitation at flowering stage showed positive effects. Low mean temperature and low total precipitation before bud opening, at flowering, and by fruit maturity, as well as number of freezing days (<−5 °C) before flower bud opening, showed negative effects. Soil and tissue tests, and N-P-K fertilization showed smaller effects. Gaussian processes predicted yields from historical weather data, soil test, fertilizer dosage, and tissue test with a root-mean-square-error of 1447 kg ha−1. An in-house Markov chain algorithm optimized yields modelled by Gaussian processes from tissue test, soil test, and fertilizer dosage as conditioned to specified historical meteorological features, potentially increasing yield by a median factor of 1.5. Machine learning, compositional data analysis, and Markov chains allowed customizing nutrient management of lowbush blueberry at local scale

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Vegetable Response to Added Nitrogen and Phosphorus Using Machine Learning Decryption and the N/P Ratio

The current N and P fertilization practices for vegetable crops grown in organic soils are inaccurate and and may potentially damage the environment. New fertilization models are needed. Machine learning (ML) methods can combine numerous features to predict crop response to N and P fertilization. Our objective was to evaluate machine learning predictions for marketable yields, N and P offtakes, and the N/P ratio of vegetable crops. We assembled 157 multi-environmental fertilizer trials on lettuce (Lactuca sativa), celery (Apium graveolens), onion (Allium cepa), and potato (Solanum tuberosum) and documented 22 easy-to-collect soil, managerial, and meteorological features. The random forest models returned moderate to substantial strength (R2 = 0.73–0.80). Soil and managerial features were the most important. There was no response to added P and null to moderate response to added N in independent universality tests. The N and P offtakes were most impacted by P-related features, indicating N–P interactions. The N/P mass ratios of harvested products were generally lower than 10, suggesting P excess that would trigger plant N acquisition and possibly alter soil N and C cycles through microbial processes. Crop response prediction by ML models and ex post N/P ratio diagnosis and N and P offtakes proved to be useful tools to guide N and P management decisions in organic soils

Un seuil de la douleur, est-ce que cela existe ? Is there really such a thing as a pain threshold?

With conventional methods of determining sensory perception thresholds, the experimenter takes all possible steps to avoid making incorrect decisions and only keeps correct decisions. However, in a decision-making process one has to distinguish two aspects: sensitivity, i.e. how well the observer is able to make correct judgments and avoid incorrect ones and bias, i.e. the extent to which the observer favors one hypothesis over another, independently of the sensory information he has received. Sensory detection theory (SDT) offers the theoretical foundations and the practical means for estimating these two aspects of detection performance, which in the past have often been confused, leading to mistakes in interpreting behavior. The advantages of SDT are illustrated with practical examples

Retention, hydrolysis and plant availability of pyrophosphate applied to organic soil material

Pyrophosphate retention and half-life values, and pyrophosphatase activity, were determined in 24 organic soil materials containing < 20% ash. Pyrophosphate retention was correlated with ash content (r = 0,876**) but still was weak. Pyrophosphatase activity (11,6 to 148,1 mmol.kg('-1).2h('-1)) was higher in virgin than in cultivated materials and was promoted apparently by nonspecific acid phosphatases. The interaction between water-soluble pyrophosphate and pyrophosphatase activity explained 77% of the variation in log-transformed half-life values ranging from 0,1 to 3,7 days. Copper decreased significantly pyrophosphatase activity. However, pyrophosphate hydrolysis rate was not affected significantly by Cu contents up to 1177 mg.kg('-1) in humic materials. Because of rapid rates of pyrophosphate hydrolysis in humic and mesic materials compared with rate of P uptake by onions, no significant difference in bulb yield and P uptake were obtained at harvest between pyrophosphate and orthophosphate fertilizers