A Bayesian approach for estimating the uncertainty on the contribution of nitrogen fixation and calculation of nutrient balances in grain legumes

Background: The proportion of nitrogen (N) derived from the atmosphere (Ndfa) is a fundamental component of the plant N demand in legume species. To estimate the N benefit of grain legumes for the subsequent crop in the rotation, a simplified N balance is frequently used. This balance is calculated as the difference between fixed N and removed N by grains. The Ndfa needed to achieve a neutral N balance (hereafter ) is usually estimated through a simple linear regression model between Ndfa and N balance. This quantity is routinely estimated without accounting for the uncertainty in the estimate, which is needed to perform formal statistical inference about . In this article, we utilized a global database to describe the development of a novel Bayesian framework to quantify the uncertainty of . This study aimed to (i) develop a Bayesian framework to quantify the uncertainty of , and (ii) contrast the use of this Bayesian framework with the widely used delta and bootstrapping methods under different data availability scenarios. Results: The delta method, bootstrapping, and Bayesian inference provided nearly equivalent numerical values when the range of values for Ndfa was thoroughly explored during data collection (e.g., 6–91%), and the number of observations was relatively high (e.g., ). When the Ndfa tested was narrow and/or sample size was small, the delta method and bootstrapping provided confidence intervals containing biologically non-meaningful values (i.e.  100%). However, under a narrow Ndfa range and small sample size, the developed Bayesian inference framework obtained biologically meaningful values in the uncertainty estimation. Conclusion: In this study, we showed that the developed Bayesian framework was preferable under limited data conditions ─by using informative priors─ and when uncertainty estimation had to be constrained (regularized) to obtain meaningful inference. The presented Bayesian framework lays the foundation not only to conduct formal comparisons or hypothesis testing involving , but also to learn about its expected value, variance, and higher moments such as skewness and kurtosis under different agroecological and crop management conditions. This framework can also be transferred to estimate balances for other nutrients and/or field crops to gain knowledge on global crop nutrient balances.Fil: Palmero, Francisco. Kansas State University; Estados UnidosFil: Hefley, Trevor J.. Kansas State University; Estados UnidosFil: Lacasa, Josefina. Kansas State University; Estados UnidosFil: Almeida, Luiz Felipe. Kansas State University; Estados UnidosFil: Haro, Ricardo J.. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Córdoba. Estación Experimental Agropecuaria Manfredi; ArgentinaFil: Garcia, Fernando Oscar. No especifíca;Fil: Salvagiotti, Fernando. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Oliveros; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Ciampitti, Ignacio Antonio. Kansas State University; Estados Unido

Spatiotemporal Characteristics of the Largest HIV-1 CRF02_AG Outbreak in Spain: Evidence for Onward Transmissions

Background and Aim: The circulating recombinant form 02_AG (CRF02_AG) is the predominant clade among the human immunodeficiency virus type-1 (HIV-1) non-Bs with a prevalence of 5.97% (95% Confidence Interval-CI: 5.41–6.57%) across Spain. Our aim was to estimate the levels of regional clustering for CRF02_AG and the spatiotemporal characteristics of the largest CRF02_AG subepidemic in Spain.Methods: We studied 396 CRF02_AG sequences obtained from HIV-1 diagnosed patients during 2000–2014 from 10 autonomous communities of Spain. Phylogenetic analysis was performed on the 391 CRF02_AG sequences along with all globally sampled CRF02_AG sequences (N = 3,302) as references. Phylodynamic and phylogeographic analysis was performed to the largest CRF02_AG monophyletic cluster by a Bayesian method in BEAST v1.8.0 and by reconstructing ancestral states using the criterion of parsimony in Mesquite v3.4, respectively.Results: The HIV-1 CRF02_AG prevalence differed across Spanish autonomous communities we sampled from (p < 0.001). Phylogenetic analysis revealed that 52.7% of the CRF02_AG sequences formed 56 monophyletic clusters, with a range of 2–79 sequences. The CRF02_AG regional dispersal differed across Spain (p = 0.003), as suggested by monophyletic clustering. For the largest monophyletic cluster (subepidemic) (N = 79), 49.4% of the clustered sequences originated from Madrid, while most sequences (51.9%) had been obtained from men having sex with men (MSM). Molecular clock analysis suggested that the origin (tMRCA) of the CRF02_AG subepidemic was in 2002 (median estimate; 95% Highest Posterior Density-HPD interval: 1999–2004). Additionally, we found significant clustering within the CRF02_AG subepidemic according to the ethnic origin.Conclusion: CRF02_AG has been introduced as a result of multiple introductions in Spain, following regional dispersal in several cases. We showed that CRF02_AG transmissions were mostly due to regional dispersal in Spain. The hot-spot for the largest CRF02_AG regional subepidemic in Spain was in Madrid associated with MSM transmission risk group. The existence of subepidemics suggest that several spillovers occurred from Madrid to other areas. CRF02_AG sequences from Hispanics were clustered in a separate subclade suggesting no linkage between the local and Hispanic subepidemics

A Bayesian approach for estimating and checking block designs in agricultural experiments

Master of ScienceDepartment of StatisticsTrevor HefleySpatial blocking is a common technique used in designed agricultural experiments. Spatial blocking, however, requires two important assumptions: 1) the spatial blocks can be clearly delineated within a field by an expert; and 2) the area within each block is homogeneous. Statistical analyses of blocked agricultural experiments often show spatially correlated residuals, suggesting one of the assumptions mentioned above was not met. We propose a model for estimating block membership with data, as an alternative method to account for spatial effects, while preserving the traditional designed experiment framework. We embed a classification and regression tree within a Bayesian statistical model, to estimate block membership. We illustrate possible applications of this approach with some of the most typical scenarios we have encountered in our applied experience in agricultural research with four synthetic data sets and two field data sets. Our hybrid Bayesian-Machine Learning approach can serve one of two purposes: validating the originally designed block layout, or estimating the spatial block layout. Thus, this model provides researchers with a flexible tool for analyzing designed agricultural experiments

Rethinking crop nutrition diagnosis models: methods, inference and practical applications in crop production and breeding

Doctor of PhilosophyDepartment of AgronomyIgnacio CiampittiFertilizer management is one of the most important aspects of agronomic management, affecting the system's sustainability, and relies heavily on the statistical models for nutritional diagnoses. While under-fertilizing may penalize crop yields, excessive fertilization is linked to negative environmental externalities. It is thus desirable that the fertilizer rates approximately match the crop nutrient requirements. Because crop nutrient requirements are usually associated to crop growth, it is convenient to monitor the nutritional status to determine the amount of nutrient required according to crop growth. With the increase in computational power of mainstream computers, many popular crop nutrition models are undergoing changes that leverage such advances in technology. This dissertation is organized in six chapters (Chapter 1, Introduction, and Chapter 6, Final remarks) that revise, rethink, and expand some of the most popular models applied to crop nutrition management. Chapters 2-3 are oriented to the statistical inference, and portray methods developments that may help improve inference from crop nutrition models. Chapter 2 portrays some advantages enabled by modern statistical computing tools by comparing a standard statistical framework introduced in the 1990s, versus a modern statistical framework introduced in 2020. Chapter 3 follows up on the findings of Chapter 2 and elaborates the model and establishes prospects for statistical modeling of crop nutrition models with current statistical tools. Chapters 4-5 are oriented to the practical application of crop nutrition models and the integration of modern measuring hardware, and portray methods for applications in phenotyping and plant breeding settings. Chapter 4 compares different metrics for quantifying crop nutritional status for breeding applications in wheat (\textit{Triticum aestivum} L.). Chapter 5 identifies avenues for research for further developing methods and measuring devices for crop nutritional status phenotyping. While most of the crop nutrition problems presented in this dissertation consider nitrogen management, these finding are relevant for other nutrients as well

A probabilistic framework for forecasting maize yield response to agricultural inputs with sub-seasonal climate predictions

Crop yield results from the complex interaction between genotype, management, and environment. While farmers have control over what genotype to plant and how to manage it, their decisions are often sub-optimal due to climate variability. Sub-seasonal climate predictions embrace the great potential to improve risk analysis and decision-making. However, adequate frameworks integrating future weather uncertainty to predict crop outcomes are lacking. Maize (Zea mays L.) yields are highly sensitive to weather anomalies, and very responsive to plant density (plants m ^−2 ). Thus, economic optimal plat density is conditional to the seasonal weather conditions and can be anticipated with seasonal prospects. The aims of this study were to (i) design a model that describes the yield-to-plant density relationship (herein termed as yield–density) as a function of weather variables, and provides probabilistic forecasts for the economic optimum plant density (EOPD), and (ii) analyze the model predictive performance and sources of uncertainty. We present a novel approach to enable decision-making in agriculture using sub-seasonal climate predictions and Bayesian modeling. This model may inform crop management recommendations and accounts for various sources of uncertainty. A Bayesian hierarchical shrinkage model was fitted to the response of maize yield–density trials performed during the 2010–2019 period across seven states in the United States, identifying the relative importance of key weather, crop, and soil variables. Tercile forecasts of precipitation and temperature from the International Research Institute were used to forecast EOPD before the start of the season. The variables with the greatest influence on the yield–density relationship were weather anomalies, especially those variables indicating months with above-normal temperatures. Improvements on climate forecasting may also improve forecasts on yield responses to management, as we found reduced bias and error (by a factor >10), and greater precision (e.g. R ^2 increased from 0.26 to 0.32) for cases where weather forecasts matched observations. This study may contribute to the development of decision-support tools that can trigger discussions between farmers and consultants about management strategies and their associated risks

A practical guide to estimating the light extinction coefficient with nonlinear models—a case study on maize

Abstract Background The fraction of intercepted photosynthetically active radiation (fPARi) is typically described with a non-linear function of leaf area index (LAI) and k, the light extinction coefficient. The parameter k is used to make statistical inference, as an input into crop models, and for phenotyping. It may be estimated using a variety of statistical techniques that differ in assumptions, which ultimately influences the numerical value k and associated uncertainty estimates. A systematic search of peer-reviewed publications for maize (Zea Mays L.) revealed: (i) incompleteness in reported estimation techniques; and (ii) that most studies relied on dated techniques with unrealistic assumptions, such as log-transformed linear models (LogTLM) or normally distributed data. These findings suggest that knowledge of the variety and trade-offs among statistical estimation techniques is lacking, which hinders the use of modern approaches such as Bayesian estimation (BE) and techniques with appropriate assumptions, e.g. assuming beta-distributed data. Results The parameter k was estimated for seven maize genotypes with five different methods: least squares estimation (LSE), LogTLM, maximum likelihood estimation (MLE) assuming normal distribution, MLE assuming beta distribution, and BE assuming beta distribution. Methods were compared according to the appropriateness for statistical inference, point estimates’ properties, and predictive performance. LogTLM produced the worst predictions for fPARi, whereas both LSE and MLE with normal distribution yielded unrealistic predictions (i.e. fPARi  1) and the greatest coefficients for k. Models with beta-distributed fPARi (either MLE or Bayesian) were recommended to obtain point estimates. Conclusion Each estimation technique has underlying assumptions which may yield different estimates of k and change inference, like the magnitude and rankings among genotypes. Thus, for reproducibility, researchers must fully report the statistical model, assumptions, and estimation technique. LogTLMs are most frequently implemented, but should be avoided to estimate k. Modeling fPARi with a beta distribution was an absent practice in the literature but is recommended, applying either MLE or BE. This workflow and technique comparison can be applied to other plant canopy models, such as the vertical distribution of nitrogen, carbohydrates, photosynthesis, etc. Users should select the method balancing benefits and tradeoffs matching the purpose of the study

Breeding effects on canopy light attenuation in maize: A retrospective and prospective analysis

The light attenuation process within a plant canopy defines energy capture and vertical distribution of light and nitrogen (N). The vertical light distribution can be quantitatively described with the extinction coefficient (k), which associates the fraction of intercepted photosynthetically active radiation (fPARi) with the leaf area index (LAI). Lower values of k correspond to upright leaves and homogeneous vertical light distribution, increasing radiation use efficiency (RUE). Yield gains in maize (Zea mays L.) were accompanied by increases in optimum plant density and leaf erectness. Thus, the yield-driven breeding programs and management changes, such as reduced row spacing, selected a more erect leaf habit under different maize production systems (e.g., China and the USA). In this study, data from Argentina revealed that k decreased at a rate of 1.1% year–1 since 1989, regardless of plant density and in agreement with Chinese reports (1.0% year–1 since 1981). A reliable assessment of changes in k over time is critical for predicting (i) modifications in resource use efficiency (e.g. radiation, water, and N), improving estimations derived from crop simulation models; (ii) differences in productivity caused by management practices; and (iii) limitations to further exploit this trait with breeding.Fil: Lacasa, Josefina. Kansas State University; Estados Unidos. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; ArgentinaFil: Ciampitti, Ignacio Antonio. Kansas State University; Estados UnidosFil: Amás, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino; ArgentinaFil: Curin, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires. Universidad Nacional del Noroeste de la Provincia de Buenos Aires. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires; ArgentinaFil: Luque, Sergio Fernando. Universidad Nacional de Córdoba. Facultad de Cs.agropecuarias. Departamento de Producción Vegetal; ArgentinaFil: Otegui, Maria Elena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires. Universidad Nacional del Noroeste de la Provincia de Buenos Aires. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal. Cátedra de Producción Vegetal; Argentin

Breeding effects on canopy light attenuation in maize: a retrospective and prospective analysis

The light attenuation process within a plant canopy defines energy capture and vertical distribution of light and nitrogen (N). The vertical light distribution can be quantitatively described with the extinction coefficient (k), which associates the fraction of intercepted photosynthetically active radiation (fPARi) with the leaf area index (LAI). Lower values of k correspond to upright leaves and homogeneous vertical light distribution, increasing radiation use efficiency (RUE). Yield gains in maize (Zea mays L.) were accompanied by increases in optimum plant density and leaf erectness. Thus, the yield-driven breeding programs and management changes, such as reduced row spacing, selected a more erect leaf habit under different maize production systems (e.g., China and the USA). In this study, data from Argentina revealed that k decreased at a rate of 1.1% year–1 since 1989, regardless of plant density and in agreement with Chinese reports (1.0% year–1 since 1981). A reliable assessment of changes in k over time is critical for predicting (i) modifications in resource use efficiency (e.g. radiation, water, and N), improving estimations derived from crop simulation models; (ii) differences in productivity caused by management practices; and (iii) limitations to further exploit this trait with breeding.EEA PergaminoFil: Lacasa, Josefina. Kansas State University. Throckmorton Plant Science Center. Department of Agronomy; Estados UnidosFil: Lacasa, Josefina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; ArgentinaFil: Ciampitti, Ignacio. Kansas State University. Throckmorton Plant Science Center. Department of Agronomy; Estados UnidosFil: Amas, Juan I. Instituto Nacional de Tecnología Agropecuaria (INTA). Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino; ArgentinaFil: Amas, Juan I. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET); ArgentinaFil: Curín, Facundo. Centro de Investigaciones y Transferencias del Noroeste de la Provincia de Buenos Aires (CIT-NOBA-CONICET); ArgentinaFil: Luque, Sergio F. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Cátedra de Cereales y Oleaginosas; ArgentinaFil: Otegui, María E. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET); ArgentinaFil: Otegui, María E. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal; ArgentinaFil: Otegui, María Elena. Instituto Nacional de Tecnología Agropecuaria (INTA). Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino; Argentin

NNI Dataset updated

This dataset is an update of the dataset published by Ciampitti et al., 2022 (A global dataset to parametrize critical nitrogen dilution curves for major crop species). This dataset contains the result of extracting data of plant biomass, plant N concentration and harvest yield.</p

Revisiting the relationship between nitrogen nutrition index and yield across major species

Crop nitrogen (N) fertilization diagnoses via the N nutrition index (NNI)-yield relationship have been tested forseveral crop species, but a cross-species comparison of that relationship has not been performed yet. This studyaimed to perform a cross-species comparison of the relationship between NNI and yield with emphasis on theyield sensitivity to N deficiency, slope of the models. Additionally, we conducted an evaluation to determine thebest NNI sampling moment to predict relative yield, with focus on major grain crops. Based on a recently publishedglobal dataset to parametrize critical dilution curves, we calculated integrated NNI, instantaneous NNI,relative yield, and relative shoot biomass for annual ryegrass, tall fescue, maize, potato, rice, and wheat. Weobtained 238 observations to fit integrated NNI-relative yield linear mixed-effects models and 1606 observationsto fit instantaneous NNI-relative yield models. Subsequently, we performed a sensitivity analysis to determinethe best NNI sampling moment to predict relative yield, with focus on major grain crops (maize, rice, and wheat).Our results show that there was low inter-species variation of sensitivity to N deficiency, i.e., the slope of therelationship between relative yield and integrated NNI. For grain crops, instantaneous NNI around anthesisdemonstrated a better predictive capability for relative yield, outperforming other vegetative stages. This findingcontributed to improving the understanding of the association between relative yield and NNI with implicationsfor breeding programs, nutrient management practices, and crop modelling. Most importantly, this study is acontribution to improving the N nutrition diagnosis for several crop species, by using an integral, comparativeapproach.Fil: Rodriguez, Ignacio Martin. Universidad Nacional de Mar del Plata; Argentina. Kansas State University; Estados Unidos. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce. Agencia de Extensión Rural Balcarce; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; ArgentinaFil: Lacasa, Josefina. Kansas State University; Estados UnidosFil: van Versendaal, Emmanuela. Kansas State University; Estados UnidosFil: Lemaire, Gilles. Institut National de la Recherche Agronomique; FranciaFil: Belanger, Gilles. Quebec Research And Development Centre; CanadáFil: Jégo, Guillaume. Quebec Research And Development Centre; CanadáFil: Sandaña, Patricio G.. Universidad Austral de Chile; ChileFil: Soratto, Rogério P.. Universidade Federal de Sao Paulo; BrasilFil: Djalovic, Ivica. National Institute of the Republic of Serbia; SerbiaFil: Ata Ul Karim, Syed Tahir. State University of Pennsylvania; Estados UnidosFil: Reussi Calvo, Nahuel Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Universidad Nacional de Mar del Plata; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce. Agencia de Extensión Rural Balcarce; ArgentinaFil: Giletto, Claudia Marcela. Universidad Nacional de Mar del Plata; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce. Agencia de Extensión Rural Balcarce; ArgentinaFil: Zhao, Ben. Chinese Academy Of Agricultural Sciences; ChinaFil: Ciampitti, Ignacio Antonio. Kansas State University; Estados Unido