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Development of predictive algorithms accounting for uncertainty in processes underpinning the maize (Zea mays L.) yield response to nitrogen (N) are needed in order to provide new N fertilization guidelines. The aims of this study were to unravel the relative importance of crop management, soil, and weather factors on both the estimate and the size of uncertainty (as a risk magnitude assessment) of the main components of the maize yield response to N: i) yield without N fertilizer (B0); ii) yield at economic optimum N rate (YEONR); iii) EONR; and iv) the N fertilizer efficiency (NFE) at the EONR. Combining Bayesian statistics to fit the N response curves and a machine learning algorithm (extreme gradient boosting) to assess features importance on the predictability of the process, we analyzed data of 730 response curves from 481 site-years (4297 observations) in maize N rate fertilization studies conducted between 1999 and 2020 in the United States and Canada. The EONR was the most difficult attribute to predict, with an average uncertainty of 50 kg N ha−1, increasing towards low ( 200 kg N ha−1) EONR expected values. Crop management factors such as previous crop and irrigation contributed substantially (∼50%) to the estimation of B0, but minorly to other components of the maize yield response to N. Weather contributed about two-thirds of explained variance of the estimated values of YEONR, EONR, and NFE. Additionally, weather factors governed the uncertainty (72% to 81%) of all components of the N response process. Soil factors provided a consistent but limited (10% to 23%) contribution to explain both expected N response as well as its associated uncertainties. Efforts to improve N decision support tools should consider the uncertainty of models as a type of risk, potential in-season weather scenarios, and develop probabilistic frameworks for improving this data-driven decision-making process of N fertilization in maize crop.

The introduction of winter canola (Brassica napus L.) into rotations with winter wheat (Triticum aestivum L.) in the United States (US) revealed economic and agronomic benefits as well as improved weed and pest control. Canola stand establishment during the fall and plant survival over the winter are critical to the success of this crop. The environment plays a key role influencing survival, but in-depth evaluation of meteorological factors has not been conducted. This research study aimed to: i) identify meteorological factors underpinning winter canola survival, ii) build probabilistic response models based on historical meteorological data for different severities of winter kill across the US, and iii) define areas of adaptation of current germplasm. A winter survival dataset was compiled from the National Winter Canola Variety Trials from 2003 until 2018 (190 site-years) and auxiliary meteorological data over the last 40 years. A regression tree analysis indicated that meteorological variables related to minimum temperature, fluctuating temperatures above and below 0°C, and windchill during the cold period were the main factors accounting for winter kill. Cold periods across all site-years were classified into three clusters: cold periods with high (96%), medium (70%), and low (28%) average plant winter survival. For 94 US sites, the probabilities of these conditions were calculated and summarized in a map that defined areas of adaptation: a large area south of 35° N latitude for the US was identified with greater potential for overwintering success. Based on the response under multiple meteorological conditions, four distinct genotype survival groups were identified (tolerant, semi-tolerant, semi-susceptible, and susceptible). Groups with a greater number of genotypes differ in the impact of meteorological conditions on survival for the medium cluster. In regions with more favorable conditions for overwintering success, farmers may be open to introducing this crop to diversify their farming system.

A risk assessment framework is presented to characterize the vulnerability of agricultural production systems to El Nino-southern oscillation (ENSO)-related climate variability. The framework was applied to current maize production systems in two locations (Pergamino and Pilar) in the Pampas of central-eastern Argentina. Climatic, agronomic, and economic models were linked to produce probability distributions of farm-level yields and net returns by ENSO phase. Generally, an enhanced chance of higher (lower) simulated maize yields existed during warm (cold) ENSO events. However, regional differences existed: the effect of warm events on yields was more marked in Pilar, but Pergamino showed a proportionally stronger response to cold events. The modeling framework allowed the exploration of outcomes of high and low scenarios of soil water availability at planting time and ENSO phase. High initial soil water availability in Pilar offset increased yield risks from dry conditions associated with cold ENSO events. Fluctuations of output prices were shown to have considerable influence on the risk associated with ENSO-related climate variability. Despite these general results, there was considerable overlap in yields and net returns for the various ENSO phases. This overlap has significant implications for the adoption of ENSO forecasts in agriculture. The risk assessment framework developed here is a necessary precursor to risk management studies that prescribe or describe possible responses to expected climate scenarios.