Prediction of Nitrogen Responses of Corn by Soil Nitrogen Mineralization Indicators

Soil nitrogen mineralization potential (Nmin) has to be spatially quantified to enable farmers to vary N fertilizer rates, optimize crop yields, and minimize N transfer from soils to the environment. The study objectives were to assess the spatial variability in soil Nmin potential based on clay and organic matter (OM) contents and the impact of grouping soils using these criteria on corn grain (Zea mays L.) yield, N uptake response curves to N fertilizer, and soil residual N. Four indicators were used: OM content and three equations involving OM and clay content. The study was conducted on a 15-ha field near Montreal, Quebec, Canada. In the spring 2000, soil samples (n = 150) were collected on a 30- x 30-m grid and six rates of N fertilizer (0 to 250 kg N ha-1) were applied. Kriged maps of particle size showed areas of clay, clay loam, and fine sandy loam soils. The Nmin indicators were spatially structured but soil nitrate (NO3–) was not. The N fertilizer rate to reach maximum grain yield (Nmax), as estimated by a quadratic model, varied among textural classes and Nmin indicators, and ranged from 159 to 250 kg N ha-1. The proportion of variability (R2) and the standard error of the estimate (SE) varied among textural groups and Nmin indicators. The R2 ranged from 0.53 to 0.91 and the SE from 0.13 to 1.62. Corn grain N uptake was significantly affected by N fertilizer and the pattern of response differed with soil texture. For the 50 kg N ha-1 rate, the apparent Nmin potential (ANM) was significantly larger in the clay loam (122 kg ha-1) than in the fine sandy loam (80 kg ha-1) or clay (64 kg ha-1) soils. The fall soil residual N was not affected by N fertlizer inputs. Textural classes can be used to predict Nmax. The Nmin indicators may also assist the variable rate N fertilizer inputs for corn production

Determining the Beginning of Potato Tuberization Period Using Plant Height Detected by Drone for Irrigation Purposes

Insolation and precipitation instability associated with climate change affects plant development patterns and water demand. The potato root system and soil properties lead to water vulnerability, impacting crop yield. Regarding potato physiology, plants stop growing when the root depth stabilizes, and then the tuberization period begins. Since this moment, water supply is required. Consequently, an approach based on plant physiology may enable farmers to detect the beginning of the irrigation period precisely. Remote sensing is a fast and precise method for obtaining surface information using non-invasive data collection. The database comprises root depth (RD) and plant height (H) data collected during 2019, 2020, and 2021. This research aims to develop a dynamic approach based on remote sensing and crop physiology to accurately determine the beginning of the tuberization period, called here the irrigation critical point (ICP). The results indicate a high correlation between RD and H (>0.85) which is independent of in-field soil and relief variations > 0.95). Further, plant growth rate corroborates the correlation results with decreasing patterns in time (R2 > 0.80), independent of environmental variations. In short, it was possible to determine the ICP based on the crop growth dynamics, independently of climate variations, field placement, or irrigation system

Cultivar-specific nutritional status of potato (Solanum tuberosum L.) crops.

Gradients in the elemental composition of a potato leaf tissue (i.e. its ionome) can be linked to crop potential. Because the ionome is a function of genetics and environmental conditions, practitioners aim at fine-tuning fertilization to obtain an optimal ionome based on the needs of potato cultivars. Our objective was to assess the validity of cultivar grouping and predict potato tuber yields using foliar ionomes. The dataset comprised 3382 observations in Québec (Canada) from 1970 to 2017. The first mature leaves from top were sampled at the beginning of flowering for total N, P, K, Ca, and Mg analysis. We preprocessed nutrient concentrations (ionomes) by centering each nutrient to the geometric mean of all nutrients and to a filling value, a transformation known as row-centered log ratios (clr). A density-based clustering algorithm (dbscan) on these preprocessed ionomes failed to delineate groups of high-yield cultivars. We also used the preprocessed ionomes to assess their effects on tuber yield classes (high- and low-yields) on a cultivar basis using k-nearest neighbors, random forest and support vector machines classification algorithms. Our machine learning models returned an average accuracy of 70%, a fair diagnostic potential to detect in-season nutrient imbalance of potato cultivars using clr variables considering potential confounding factors. Optimal ionomic regions of new cultivars could be assigned to the one of the closest documented cultivar

Tillage Management Impacts on Soil Phosphorus Variability under Maize–Soybean Rotation in Eastern Canada

Conservation tillage, including no-tillage (NT), is being used increasingly with respect to conventional tillage (CT) to mitigate soil erosion, improve water conservation and prevent land degradation. However, NT increases soil phosphorus (P) stratification, causing P runoff and eutrophication. For sustainable P management, fertilization must be balanced between P sources and actual crop demand. To reduce P losses to the environment, it is important to better understand P spatial variability in NT fields. Little is known about tillage impacts on field-scale P spatial variabi-lity in precision agriculture. This study examines tillage impacts on spatial variability of soil-avai-lable P in a maize–soybean rotation, in two commercial fields, denoted CT (10.8 ha) and NT (9.5 ha), with the aim of improving P fertilizer recommendations in Eastern Canada. NPK fertilizers were applied to the soils (Humic Gleysols) following local recommendations. Soil samples were collected in fall 2014 in regular 35 m by 35 m grids, at 0–5 and 5–20 cm depths, providing 141 and 134 geore-ferenced points for CT and NT fields, respectively. Available P and other elements were analyzed by Mehlich-3 extraction (M3), and the P saturation index (P/Al)M3 was calculated. Variability of soil-available P in both fields ranged from moderate to very high (32% to 60%). A mean (P/Al)M3 of 3% was found in both layers under CT, compared to 8% in the 0–5 cm layer and 6% in the 5–20 cm layer under NT. Relationships between P indices and other elements differed between tillage practices. This study highlights the need to improve P fertilizer recommendations in Eastern Canada

Site-specific machine learning predictive fertilization models for potato crops in Eastern Canada.

Statistical modeling is commonly used to relate the performance of potato (Solanum tuberosum L.) to fertilizer requirements. Prescribing optimal nutrient doses is challenging because of the involvement of many variables including weather, soils, land management, genotypes, and severity of pests and diseases. Where sufficient data are available, machine learning algorithms can be used to predict crop performance. The objective of this study was to determine an optimal model predicting nitrogen, phosphorus and potassium requirements for high tuber yield and quality (size and specific gravity) as impacted by weather, soils and land management variables. We exploited a data set of 273 field experiments conducted from 1979 to 2017 in Quebec (Canada). We developed, evaluated and compared predictions from a hierarchical Mitscherlich model, k-nearest neighbors, random forest, neural networks and Gaussian processes. Machine learning models returned R2 values of 0.49-0.59 for tuber marketable yield prediction, which were higher than the Mitscherlich model R2 (0.37). The models were more likely to predict medium-size tubers (R2 = 0.60-0.69) and tuber specific gravity (R2 = 0.58-0.67) than large-size tubers (R2 = 0.55-0.64) and marketable yield. Response surfaces from the Mitscherlich model, neural networks and Gaussian processes returned smooth responses that agreed more with actual evidence than discontinuous curves derived from k-nearest neighbors and random forest models. When conditioned to obtain optimal dosages from dose-response surfaces given constant weather, soil and land management conditions, some disagreements occurred between models. Due to their built-in ability to develop recommendations within a probabilistic risk-assessment framework, Gaussian processes stood out as the most promising algorithm to support decisions that minimize economic or agronomic risks