Prediction of sunflower grain oil concentration as a function ofvariety, crop management and environment using statistical models

Sunflower (Helianthus annuus L.) raises as a competitive oilseed crop in the current environmentallyfriendly context. To help targeting adequate management strategies, we explored statistical models astools to understand and predict sunflower oil concentration. A trials database was built upon experi-ments carried out on a total of 61 varieties over the 2000–2011 period, grown in different locations inFrance under contrasting management conditions (nitrogen fertilization, water regime, plant density).25 literature-based predictors of seed oil concentration were used to build 3 statistical models (multiplelinear regression, generalized additive model (GAM), regression tree (RT)) and compared to the refer-ence simple one of Pereyra-Irujo and Aguirrezábal (2007) based on 3 variables. Performance of modelswas assessed by means of statistical indicators, including root mean squared error of prediction (RMSEP)and model efficiency (EF). GAM-based model performed best (RMSEP = 1.95%; EF = 0.71) while the simplemodel led to poor results in our database (RMSEP = 3.33%; EF = 0.09). We computed hierarchical contribu-tion of predictors in each model by means of R2and concluded to the leading determination of potentialoil concentration (OC), followed by post-flowering canopy functioning indicators (LAD2 and MRUE2),plant nitrogen and water status and high temperatures effect. Diagnosis of error in the 4 statistical mod-els and their domains of applicability are discussed. An improved statistical model (GAM-based) wasproposed for sunflower oil prediction on a large panel of genotypes grown in contrasting environments

The Relationship Between Economically and Environmentally Marginal Land

Concerns have frequently been raised regarding the impact of federally-subsidized crop insurance and agricultural subsidy payments on land allocation and crop mix choices. If the reduction in production risk encourages farmers to plant on economically marginal land, it has often been asserted that this will lead to increases in environmental damage, including increases in soil erosion rates. This paper investigates the "conventional" wisdom that economically marginal land is also environmentally fragile, as defined by higher levels of inherent soil erodibility. We address this issue by looking at the distribution of crop yields for 4 major crops across National Resource Inventory (NRI) erodibility classes and by performing regression analysis. Our results indicate that land with higher levels of soil erodibility exhibit lower mean crop yields, a proxy for economic marginality, which lends support to the conventional wisdom.Environmental Economics and Policy,

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing

Livestock in diverse cropping systems improve weed management and sustain yields whilst reducing inputs

<p>Dataset used for the article: </p> <p>MacLaren, C.; Storkey, J.; Strauss, J.; Swanepoel, P.; and Dehnen-Schmutz, K. (2018). Livestock in diverse cropping systems improve weed management and sustain yields whilst reducing inputs. <em>Journal of Applied Ecology.</em></p> <p> </p

Multivariate methods in aquaculture research: case studies of tilapias in experimental and commercial systems

This volume documents the usefulness of multivariate methods û notably multiple regression, path analysis and canonical correlation û in the context of aquaculture, which has to date tended to neglect such methods, and hence to underutilize available data. All examples used here stem from experimental and/or commercial tilapia culture systems, and hence this book also represents an advance in the understanding of such systems.Aquaculture, Tilapia culture, Growth, Multivariate analysis Oreochromis

Ecological models at fish community and species level to support effective river restoration

RESUMEN Los peces nativos son indicadores de la salud de los ecosistemas acuáticos, y se han convertido en un elemento de calidad clave para evaluar el estado ecológico de los ríos. La comprensión de los factores que afectan a las especies nativas de peces es importante para la gestión y conservación de los ecosistemas acuáticos. El objetivo general de esta tesis es analizar las relaciones entre variables biológicas y de hábitat (incluyendo la conectividad) a través de una variedad de escalas espaciales en los ríos Mediterráneos, con el desarrollo de herramientas de modelación para apoyar la toma de decisiones en la restauración de ríos. Esta tesis se compone de cuatro artículos. El primero tiene como objetivos modelar la relación entre un conjunto de variables ambientales y la riqueza de especies nativas (NFSR), y evaluar la eficacia de potenciales acciones de restauración para mejorar la NFSR en la cuenca del río Júcar. Para ello se aplicó un enfoque de modelación de red neuronal artificial (ANN), utilizando en la fase de entrenamiento el algoritmo Levenberg-Marquardt. Se aplicó el método de las derivadas parciales para determinar la importancia relativa de las variables ambientales. Según los resultados, el modelo de ANN combina variables que describen la calidad de ribera, la calidad del agua y el hábitat físico, y ayudó a identificar los principales factores que condicionan el patrón de distribución de la NFSR en los ríos Mediterráneos. En la segunda parte del estudio, el modelo fue utilizado para evaluar la eficacia de dos acciones de restauración en el río Júcar: la eliminación de dos azudes abandonados, con el consiguiente incremento de la proporción de corrientes. Estas simulaciones indican que la riqueza aumenta con el incremento de la longitud libre de barreras artificiales y la proporción del mesohabitat de corriente, y demostró la utilidad de las ANN como una poderosa herramienta para apoyar la toma de decisiones en el manejo y restauración ecológica de los ríos Mediterráneos. El segundo artículo tiene como objetivo determinar la importancia relativa de los dos principales factores que controlan la reducción de la riqueza de peces (NFSR), es decir, las interacciones entre las especies acuáticas, variables del hábitat (incluyendo la conectividad fluvial) y biológicas (incluidas las especies invasoras) en los ríos Júcar, Cabriel y Turia. Con este fin, tres modelos de ANN fueron analizados: el primero fue construido solamente con variables biológicas, el segundo se construyó únicamente con variables de hábitat y el tercero con la combinación de estos dos grupos de variables. Los resultados muestran que las variables de hábitat son los ¿drivers¿ más importantes para la distribución de NFSR, y demuestran la importancia ecológica de los modelos desarrollados. Los resultados de este estudio destacan la necesidad de proponer medidas de mitigación relacionadas con la mejora del hábitat (incluyendo la variabilidad de caudales en el río) como medida para conservar y restaurar los ríos Mediterráneos. El tercer artículo busca comparar la fiabilidad y relevancia ecológica de dos modelos predictivos de NFSR, basados en redes neuronales artificiales (ANN) y random forests (RF). La relevancia de las variables seleccionadas por cada modelo se evaluó a partir del conocimiento ecológico y apoyado por otras investigaciones. Los dos modelos fueron desarrollados utilizando validación cruzada k-fold y su desempeño fue evaluado a través de tres índices: el coeficiente de determinación (R2 ), el error cuadrático medio (MSE) y el coeficiente de determinación ajustado (R2 adj). Según los resultados, RF obtuvo el mejor desempeño en entrenamiento. Pero, el procedimiento de validación cruzada reveló que ambas técnicas generaron resultados similares (R2 = 68% para RF y R2 = 66% para ANN). La comparación de diferentes métodos de machine learning es muy útil para el análisis crítico de los resultados obtenidos a través de los modelos. El cuarto artículo tiene como objetivo evaluar la capacidad de las ANN para identificar los factores que afectan a la densidad y la presencia/ausencia de Luciobarbus guiraonis en la demarcación hidrográfica del Júcar. Se utilizó una red neuronal artificial multicapa de tipo feedforward (ANN) para representar relaciones no lineales entre descriptores de L. guiraonis con variables biológicas y de hábitat. El poder predictivo de los modelos se evaluó con base en el índice Kappa (k), la proporción de casos correctamente clasificados (CCI) y el área bajo la curva (AUC) característica operativa del receptor (ROC). La presencia/ausencia de L. guiraonis fue bien predicha por el modelo ANN (CCI = 87%, AUC = 0.85 y k = 0.66). La predicción de la densidad fue moderada (CCI = 62%, AUC = 0.71 y k = 0.43). Las variables más importantes que describen la presencia/ausencia fueron: radiación solar, área de drenaje y la proporción de especies exóticas de peces con un peso relativo del 27.8%, 24.53% y 13.60% respectivamente. En el modelo de densidad, las variables más importantes fueron el coeficiente de variación de los caudales medios anuales con una importancia relativa del 50.5% y la proporción de especies exóticas de peces con el 24.4%. Los modelos proporcionan información importante acerca de la relación de L. guiraonis con variables bióticas y de hábitat, este nuevo conocimiento podría utilizarse para apoyar futuros estudios y para contribuir en la toma de decisiones para la conservación y manejo de especies en los en los ríos Júcar, Cabriel y Turia.Olaya Marín, EJ. (2013). Ecological models at fish community and species level to support effective river restoration [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/28853TESI

A Quantitative Structure Activity Relationship for acute oral toxicity of pesticides on rats: Validation, Domain of Application and Prediction

International audienceQuantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q2ext and the Root Mean Square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides

Reductions in global biodiversity loss predicted from conservation spending

Halting global biodiversity loss is central to both the Convention on Biological Diversity (CBD) and United Nations Sustainable Development Goals (SDGs)1,2, but success to date has been very limited3–5. A critical determinant of overall strategic success (or failure) is the financing committed to biodiversity6–9; however, financing decisions are still hindered by considerable uncertainty over what any investment is likely to achieve6–9.. For greater effectiveness, we need an evidence-based model (EBM)10–12 showing how conservation spending quantitatively reduces the rate of loss. Here, we empirically quantify how i$14.4 billion of conservation investment reduced biodiversity loss across 109 signatory countries between 1996 and 2008, by an average 29% per country. We also show that biodiversity change in signatory countries can be predicted with high accuracy, using a dual model that combines the positive impact of conservation investment with the negative impact of economic, agricultural and population growth (i.e. human development pressures)13–18. Decision-makers can use this dual model to forecast the improvement that any proposed biodiversity budget would achieve under various scenarios of human development pressure, comparing those forecasts to any chosen policy target (including the CBD and SDGs). Importantly, we further find that spending impacts shrink as human development pressures grow, implying that funding may need to increase over time. The model therefore offers a flexible tool for balancing the SDGs of human development and biodiversity, by predicting the dynamic changes needed in conservation finance as human development proceeds

Optimization and Modeling of Flow Characteristics of Low-Oil DDGS Using Regression Techniques

Citation: R. Bhadra, R. P. K. Ambrose, M. E. Casada, S. Simsek, K. Siliveru. (2017). Optimization and Modeling of Flow Characteristics of Low-Oil DDGS Using Regression Techniques. Transactions of the ASABE. 60(1): 249-258. (doi: 10.13031/trans.11928)Storage conditions, such as temperature, relative humidity (RH), consolidation pressure (CP), and time, affect the flow behavior of bulk solids such as distillers dried grains with solubles (DDGS), which is widely used as animal feed by the U.S. cattle and swine industries. The typical dry-grind DDGS production process in most corn ethanol plants has been adapted to facilitate oil extraction from DDGS for increased profits, resulting in production of low-oil DDGS. Many studies have shown that caking, and thus flow, of regular DDGS is an issue during handling and transportation. This study measured the dynamic flow properties of low-oil DDGS. Flow properties such as stability index (SI), basic flow energy (BFE), flow rate index (FRI), cohesion, Jenike flow index, and wall friction angle were measured at varying temperature (20°C, 40°C, 60°C), RH (40%, 60%, 80%), moisture content (MC; 8%, 10%, 12% w.b.), CP (generated by 0, 10, and 20 kg overbearing loads), and consolidation time (CT; 2, 4, 6, 8 days) for low-oil DDGS. Response surface modeling (RSM) and multivariate analysis showed that MC, temperature, and RH were the most influential variables on flow properties. The dynamic flow properties as influenced by environmental conditions were modeled using the RSM technique. Partial least squares regression yielded models with R2 values greater than 0.80 for SI, BFE, and cohesion as a function of MC, temperature, RH, CP, and CT using two principal components. These results provide critical information for quantifying and predicting the flow behavior of low-oil DDGS during commercial handling and transportation

Argentina-Canada from 1870: Explaining the dynamics of divergence

Argentina and Canada started their industrialization processes while exporting natural resources and importing capital goods. These two nations were sparsely populated but received significant inflows of European immigrants since the second half of the nineteenth century. Until the start of World War II, both economies experienced similar per-capita GDPs. However, the gap between both per-capita GDPs began to grow, widening throughout the century. We carry out an empirical study of the deep determinants of the divergence process between both economies. We confirm that while Canada was drawn into a successful path due to the adjacency with a bigger and complementary economy, Argentina fell into a “staple trap”.Relative per-capita GDPs, development accounting, total factor productivity, Argentina, Canada