Temperature-Driven Developmental Modulation of Yield Response to Nitrogen in Wheat and Maize

Nitrogen management is central to the economic and environmental dimensions of agricultural sustainability. Yield response to nitrogen fertilisation results from multiple interacting factors. Theoretical frameworks are lagging for the interaction between nitrogen and air temperature, the focus of this study. We analyse the relation between yield response to nitrogen fertiliser and air temperature in the critical period of yield formation for spring wheat in Australia, winter wheat in the US, and maize in both the US and Argentina. Our framework assumes (i) yield response to nitrogen fertiliser is primarily related to grain number per m2, (ii) grain number is a function of three traits: the duration of the critical period, growth rate during the critical period, and reproductive allocation, and (iii) all three traits vary non-linearly with temperature. We show that “high” nitrogen supply may be positive, neutral, or negative for yield under “high” temperature, depending on the part of the response curve captured experimentally. The relationship between yield response to nitrogen and mean temperature in the critical period was strong in wheat and weak in maize. Negative associations for both spring wheat in Australia and winter wheat with low initial soil nitrogen ( 120 kg N ha-1) that favoured grain number and compromised grain fill, the relation between yield response to nitrogen and temperature was positive for winter wheat. The framework is particularly insightful where data did not match predictions; a non-linear function integrating development, carbon assimilation and reproductive partitioning bounded the pooled data for maize in the US and Argentina, where water regime, previous crop, and soil nitrogen overrode the effect of temperature on yield response to nitrogen fertilisation.Fil: Sadras, Victor O.. University of Adelaide; Australia. South Australian Research And Development Institute; AustraliaFil: Giordano, Nicolas. Kansas State University; Estados UnidosFil: Correndo, Adrian. Kansas State University; Estados UnidosFil: Cossani, C. Mariano. University of Adelaide; Australia. South Australian Research And Development Institute; AustraliaFil: Ferreyra, Juan M.. No especifíca;Fil: Caviglia, Octavio Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos; ArgentinaFil: Coulter, Jeffrey A.. University of Minnesota; Estados UnidosFil: Ciampitti, Ignacio Antonio. Kansas State University; Estados UnidosFil: Lollato, Romulo P.. Kansas State University; Estados Unido

Predicting soil test phosphorus decrease in non-P-fertilized conditions

Monitoring the availability of phosphorus (P) in soil under continuous cropping facilitates finding deficiency in crops and contributes to improving crop growth and nutrient management models. Soil P availability for crops is usually estimated by soil test P (STP), such as Bray-1. This is widely used in the Americas. The relationship between the decrease of STP Bray-1 and cumulative removal of P was evaluated in non-P-fertilized areas in long-term studies. This removal was the sum of annual P removal over the study period as P exported in grains/crop outside the soil. The objectives were to: (a) quantify changes in STP as a function of cumulative P removal, (b) assess the relationship between relative decrease rate of STP and soil variables as well as annual removal of P by crops, and (c) develop a model to predict decrease of STP Bray-1. Exponential decay functions were used to describe annual cumulative removal of P and STP from soil over time for 12 long-term studies where no addition of P fertilizer was carried out. Changes in the relative rate of decrease of STP, relative to the initial STP Bray-1 value at the onset of the experiment, were predicted by the ratio of soil organic matter to clay and silt and the average annual P removal by exponential decay (R2adj = 0.64; RMSE = 3.2 mg kg−1). We propose this predictive model as suitable to provide estimates of the relative decrease rate of STP by Bray-1 and thereby improve management of P for optimizing crop yield. Highlights: STP Bray-1 decrease and cumulative P removal were related by exponential decay functions. Relative decrease rate of STP Bray-1 was related to SOM/(clay+silt) ratio and annual P removal. A predictive model of the relative decrease rate of STP Bray-1 was fitted and validate. Our model is a useful tool to help predict soil P availability and nutrient management.Fil: Appelhans, Stefania Carolina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Entre Ríos. Estación Experimental Agropecuaria Paraná; Argentina. Universidad Nacional de Entre Ríos; Argentina. Kansas State University; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Carciochi, Walter Daniel. Kansas State University; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Correndo, Adrian. Kansas State University; Estados UnidosFil: Gutiérrez Boem, Flavio Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones en Biociencias Agrícolas y Ambientales; ArgentinaFil: Salvagiotti, Fernando. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Oliveros; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Garcia, Fernando Oscar. International Plant Nutrition Institute; ArgentinaFil: Melchiori, Ricardo J.M.. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Entre Ríos. Estación Experimental Agropecuaria Paraná; ArgentinaFil: Barbagelata, Pedro Aníbal. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Entre Ríos. Estación Experimental Agropecuaria Paraná; Argentina. Universidad Nacional de Entre Ríos; ArgentinaFil: Ventimiglia, Luis A.. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Buenos Aires Norte. Estacion Experimental Agropecuaria Pergamino. Agencia de Extension Rural 9 de Julio.; ArgentinaFil: Ferraris, Gustavo Nestor. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino; ArgentinaFil: Vivas, Hugo S.. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Rafaela; ArgentinaFil: Caviglia, Octavio Pedro. Universidad Nacional de Entre Ríos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ciampitti, Ignacio Antonio. Kansas State University; Estados Unido

Nitrogen economy in corn-soybean farming systems

Doctor of PhilosophyDepartment of AgronomyIgnacio CiampittiNitrogen (N) is the most limiting nutrient for producing maize (Zea mays L.) and soybean [Glycine max (L.) Merr.] crops. The complex system governing the soil-plant N dynamics requires exploring multiple perspectives. Concomitantly, there is a marked need to deploy data-driven models that account for uncertainty in the processes of interest to provide improved N recommendations in both crops. Therefore, the objectives of this dissertation were: (i) to assess the contribution of environmental and crop management factors on the prediction of inherent maize productivity without N fertilizer; (ii) to identify the main drivers of both, expected values and uncertainties, of key components describing the process models for the maize yield response to N fertilizer; (iii) to summarize the impact of N and water management practices in maize grain quality; (iv) to study the residual effects of N management in maize on the following soybean crop; and, (v) to evaluate statistical techniques for the assessment of agreement between predictions and observations. In a joint effort between different academic and industry institutions in the US and Canada, a database with more than 1,200 maize N fertilization experiments (1999-2019) was built. Crop management factors such as previous crop and irrigation in combination with soil organic matter contributed to explain half of the variability of maize yield without N fertilization, while including spring weather variables (March-May) resulted in a similar performance than a framework including weather during the entire season. Crop management factors largely affected the prediction of the expected yield without N fertilizer, but just slightly impacted (<5%) the uncertainty of the response (and their components) of yield to N fertilizer. Conversely, weather variables were, undeniably, the most relevant factors and roughly contributing to 80% of the explained variance to predict the uncertainties on the yield response to N. On the other hand, a meta-analysis using a database of 92 site-years revealed that N fertilization not only increases yields but also shows a positive impact on the grain protein concentration, however, both starch and oil remained relatively constant under contrasting N fertilization levels. In contrast, water stress resulted in an erratic effect on all the evaluated grain quality components, possibly due to changes in the moment, severity, and extent of the stress. Evaluating two case studies under a maize-soybean rotation in Kansas, we documented that N fixation and soybean yields were marginally or not affected by the N management in the previous crop. Lastly, a novel and simple methodology on the use of linear regression to assess the prediction ability of simulation models is presented, also suggesting a derived decomposition of the prediction error into lack of accuracy and lack of precision along with the R-code to assist potential users. Forthcoming projects on N economy in maize and soybean farming systems should expand, provide incentives, and discuss standards in collaborative research, which represented a foundational component of this project. This dissertation highlights the advantages of deploying cutting-edge data analysis techniques for addressing research gaps on the N economy in maize-soybean farming systems. Machine learning, meta-analysis, and Bayesian statistics bring new horizons for improving forecast models as well as their interpretability. Future generations of predictive models in agriculture must be able to capture complex interactions as well as to emulate how farmers deal with uncertainties in the real world. Under this context, the awareness about uncertainties and their drivers should become one of the pillars of the dynamic N recommendations, which is crucial to convey wise information to stakeholders. Undoubtedly, we must move from static to dynamic crop models in order to design optimized GxM adaptation strategies under future climates

Soybean Yield Response to Nitrogen and Sulfur Fertilization in the United States: Contribution of Soil N and N Fixation Processes

Soybean [Glycine max (L.) Merr.] is the most important legume grown worldwide. The effect of nitrogen (N) and sulfur (S) fertilization on seed yield is commonly studied in the United States (US). However, soybean yield response to fertilization remains inconsistent, partly due to the lack of standardized field designs and a better understanding of the plant nutrition processes underpinning yield formation. The aims of this study were to assess the i) seed yield, (ii) plant N status (as N nutrition index, NNI), (iii) the contribution of N fixation, and (iv) the uncertainties on i), ii), and iii) in response to N-S fertilization using a uniform protocol across environments. Twenty-six trials in twelve US states tested five fertilization strategies that combined N and S at varying rates and timings. Using Bayesian statistics, seed yield response to fertilizer, NNI, and contribution of N fixation were analyzed at site and treatment levels providing both magnitude of responses and estimation of their uncertainties. From the significance of responses on seed yield, sites were split into two groups: non-responsive (18 sites) and responsive (8 sites). The NNI, ratio of the actual to the critical plant N concentration, was calculated to diagnose soybean N deficiency, and the N derived from the atmosphere (Ndfa, %) as N fixation contribution were investigated to better understand the source of plant N across all sites. Roughly for three-fourths of the sites, fertilization resulted in an unlikely (non-responsive) yield effect, with uncertainties ranging from 0.09 to 2.62 Mg ha−1. The other one-third of the sites were mainly responsive to S or both N + S, with the yield responses ranging from − 0.42–1.1 Mg ha−1 and uncertainties varying from 0.47 to 1.36 Mg ha−1. For the yield responsive sites, NNI presented a high proportion of deficiency (NNI\u3c1) for most of the treatments, except for the “Full” signaling to a potential for yield response. Likewise, only 6% of the changes in Ndfa were not related to the treatment “Full”, and regardless of the seed yield response to fertilization, within the same site, soil and N fixation showed similar contributions to plant N demand. Due to the high uncertainty in treatment response and contribution of N fixation, N fertilization is unlikely to increase yields, leading to non-profitable recommendations. Sulfur deficiency, on the other hand, should be explored under site-specific conditions. A decision support system should include appropriate diagnosis methods for identifying N and S deficiencies, such as NNI in soybean. Attainable maximum Ndfa did not appear to be affected by fertilization but largely varying depending on the site

A meta-analysis of hairy vetch as a previous cover crop for maize

Background: The use of hairy vetch (Vicia villosa Roth.) as cover crop is increasing worldwide. Hairy vetch can contribute as a nitrogen (N) source with potential to impact subsequent high N demanding cereals such as maize (Zea mays L.). Contrasting literature results emphasize the need for a global synthesis analysis to quantify changes in maize yield after hairy vetch. Objectives: A meta-analysis was conducted to i) quantify maize yield response to hairy vetch as previous crop, ii) explore hairy vetch influence on fertilized and non-N fertilized maize yields, and iii) assess the tillage and environment factors on maize yield response to hairy vetch. Methods: The global systematic search yielded 23 publications selected by the following criteria, i) hairy vetch dry matter at the end of the season, ii) maize grain yield, and iii) experimental design with (Mzhv) and without (Mzcontrol) hairy vetch treatments. Information such as N fertilization for maize, N accumulation in hairy vetch, organic matter, and tillage before maize sowing were recorded. Hairy vetch effects (effect size) were expressed as a ratio (percentage of grain yield variation in Mzhv/Mzcontrol). Results: Under non-N fertilization (n = 9), results revealed hairy vetch had mostly a positive effect, ranging from 13 to 45% (n = 6). In contrast, N-fertilized maize (n = 20) showed a high chance of neutral effects (n = 12), moderate probability of positive yield impact (7 to 38%, n = 6), and a low likelihood of negative effects (−32 and −17%, n = 2). Notably, maize yields improved by 21–25% when the N accumulation in hairy vetch ranged from 95 to 150 kg ha−1 and N rate from 0 to 120 kg ha−1. Non-N-fertilized maize exhibited a 14% increase in response in no-till systems and a 31% increase with conventional tillage. Conclusion: This study summarizes potential benefits of hairy vetch preceding maize. Yet, the heterogeneous outcomes deserve further exploration in terms of environment and management factors