Optimizing nitrogen fertilizer and planting density levels for maize production under current climate conditions in Northwest Ethiopian midlands

Kindie Tesfaye Dereje Ademe Enyew Adgo (2023, [Artículo])

This study determined the most effective plating density (PD) and nitrogen (N) fertilizer rate for well-adapted BH540 medium-maturing maize cultivars for current climate condition in north west Ethiopia midlands. The Decision Support System for Agrotechnology Transfer (DSSAT)-Crop Environment Resource Synthesis (CERES)-Maize model has been utilized to determine the appropriate PD and N-fertilizer rate. An experimental study of PD (55,555, 62500, and 76,900 plants ha−1) and N (138, 207, and 276 kg N ha−1) levels was conducted for 3 years at 4 distinct sites. The DSSAT-CERES-Maize model was calibrated using climate data from 1987 to 2018, physicochemical soil profiling data (wilting point, field capacity, saturation, saturated hydraulic conductivity, root growth factor, bulk density, soil texture, organic carbon, total nitrogen; and soil pH), and agronomic management data from the experiment. After calibration, the DSSAT-CERES-Maize model was able to simulate the phenology and growth parameters of maize in the evaluation data set. The results from analysis of variance revealed that the maximum observed and simulated grain yield, biomass, and leaf area index were recorded from 276 kg N ha−1 and 76,900 plants ha−1 for the BH540 maize variety under the current climate condition. The application of 76,900 plants ha−1 combined with 276 kg N ha−1 significantly increased observed and simulated yield by 25% and 15%, respectively, compared with recommendation. Finally, future research on different N and PD levels in various agroecological zones with different varieties of mature maize types could be conducted for the current and future climate periods.

Maize Model Planting Density CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE MODELS SPACING NITROGEN FERTILIZERS YIELDS

Oportunidad de aplicación de la metodología FRONT END LOADING en la expansión de gasoductos en el sureste mexicano: una evaluación

Opportunity to apply the front end loading methodology in the expansion of gas pipelines in the mexican southeast: an evaluation

Raúl Enrique Trejo Alvarado Luis Rolando Méndez Miguel (2023, [Artículo])

Con la finalidad de impulsar el desarrollo de la región sureste de México, la actual administración del gobierno federal se ha propuesto la ampliación de la red nacional de gasoductos hacia esa zona del país, con el fin de suministrar energía a los megaproyectos de la región. No obstante, estas obras presentan actualmente retrasos, evidenciando la necesidad de metodologías de gestión que permitan solucionar este problema de raíz. Una de las metodologías más prometedoras en esta materia es la front-end loading (FEL), ampliamente aplicada en el sector extractivo y energético. El propósito del articulo estriba en evaluar la posibilidad de aplicar la metodología FEL a los proyectos de expansión del Sistema Nacional de Gasoductos en el sureste mexicano.

To boost the development of the southeast region of Mexico, the current federal government administration has proposed the expansion of the national gas pipeline network to this area of the country, to supply energy to the region's mega-projects. However, these projects are currently experiencing delays, demonstrating the need for management methodologies to solve this problem at its root. One of the most promising methodologies in this area is front-end loading (FEL), which has been widely applied in the extractive and energy sector. The aim of this paper is to evaluate the possibility of applying the FEL methodology to the expansion projects of the National Gas Pipeline System in the southeastern México.

Gas natural Sector energético Front end loading (FEL) Evaluación de proyectos Sureste mexicano Natural gas Energy sector Project evaluation Mexican southeast INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS OTRAS ESPECIALIDADES TECNOLÓGICAS OTRAS OTRAS

Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding

Osval Antonio Montesinos-Lopez ABELARDO MONTESINOS LOPEZ RICARDO ACOSTA DIAZ Rajeev Varshney Jose Crossa ALISON BENTLEY (2022, [Artículo])

Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to

the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA Bayes Theorem Genome Inflammatory Bowel Diseases Models, Genetic Plant Breeding

Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy

Martin van Ittersum (2023, [Artículo])

Context: Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency. Objective: The aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers’ fields in contrasting farming systems worldwide. Methods: A large database of 10,940 field-year combinations from three countries in different stages of agricultural intensification was analyzed. Random effects models were used to partition crop yield variability and random forest models were used to explain and predict crop yield within a cross-validation scheme with data re-sampling over space and time. Results: Yield variability in relative terms was smallest for wheat and barley in the Netherlands and for wheat in Ethiopia, intermediate for rice in the Philippines, and greatest for maize in Ethiopia. Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. Crop management related variables were important to explain and predict cereal yields in Ethiopia, while predictive (i.e., known before the growing season) climatic variables and explanatory (i.e., known during or after the growing season) climatic variables were most important to explain and predict cereal yield variability in the Philippines and in the Netherlands, respectively. Finally, model cross-validation for regions or years not seen during model training reduced the R2 considerably for most crop x country combinations, while for wheat in the Netherlands this was model dependent. Conclusion: Big data from farmers’ fields is useful to explain on-farm yield variability to some extent, but not to predict it across time and space. Significance: The results call for moderate expectations towards big data and machine learning in agronomic studies, particularly for smallholder farms in the tropics where model performance was poorest independently of the variables considered and the cross-validation scheme used.

Model Accuracy Model Precision Linear Mixed Models CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MACHINE LEARNING SUSTAINABLE INTENSIFICATION BIG DATA YIELDS MODELS AGRONOMY

Towards an Emotional Validation of Heuristic Approaches for Usability Evaluation

YAZMIN MAGALLANES VELAZQUEZ ARIEL MOLINA RUEDA JOSE ALFREDO SANCHEZ HUITRON MARIBEL YENNY MENDEZ BERNAL (2012, [Artículo])

Este artículo presenta una investigación inicial sobre las maneras en que la evaluación emocional de interfaces de usuario puede extender y validar la evaluación heurística de sistemasinteractivos. Mediante la recolección de respuestas de dos grupos de usuarios, este trabajo seenfoca a detectar si existe una correlación entre la evaluación emocional de aspectos específicos de interfaces de usuario y la evaluación correspondiente realizada por expertos en interacción. Primero se obtuvo retroalimentación de expertos en interacción acerca de los problemas potenciales de una interfaz multi-táctil, con base en un enfoque heurístico. Posteriormente, se plantearon escenarios para observar a usuarios inexpertos y obtener respuestas emocionales a los aspectos considerados problemáticos por los expertos. Nuestros hallazgos iniciales indican que hay una correlación importante entre los aspectos problemáticos señalados por los expertos y las respuestas emocionales de los usuarios inexpertos, pero también que algunos de los aspectos problemáticos no necesariamente implican emociones negativas duraderas. Sugerimos entonces que una evaluación emocional de sistemas interactivos puede enriquecer y validar los enfoques heurísticos para evaluación de usabilidad.

This paper presents an initial investigation on how an emotional assessment of user interfaces may extend and validate the heuristic evaluation of the usability of interactive systems. Through the elicitation of responses from two groups of users, we focused on detecting whether a correlation exists between emotional assessment of specific interface issues and the corresponding evaluation by interaction experts. Using a prototypical multi-tactile interface and a heuristic

approach, we first obtained feedback from interaction experts regarding its potential problems and issues. We then devised scenarios for observing novel users and eliciting their emotional responses to the issues raised by our experts. Our initial findings indicate a significant correlation exists between the issues raised by expert users and the emotional responses of novel users, but also that some of those issues do not necessarily lead to long lasting negative emotions. We thus posit that emotional evaluation of interactive systems may be helpful for enriching and validating heuristic approaches for usability evaluation.

INGENIERÍA Y TECNOLOGÍA Usabilidad Evaluación emocional Evaluación Heurística Interfaces multi-táctiles Usability Emotional evaluation Heuristic evaluation Multi-tactile interfaces

A twelve years story of waste of social resources: The Mexico-Toluca Interurban Train

Rubén Chavarín (2024, [Artículo, Artículo])

The general objective of this paper is to make an ex-post evaluation of the Mexico-Toluca Interurban Train project (TIMT) more than nine years after the beginning of its construction stage. The present study is based on the cost-benefit analysis approach, through which some analysis scenarios have been built and whose results are contrasted with the official project evaluations. The present research has focused on the analysis of the TIMT as a microcosm of the scarce efficiency of public investment in Mexico, resulting in a waste of social resources. According to the results of the study, the combination of delays and cost overruns (186% in nominal terms, 114% in real terms, and 90% in dollars) has configured a project (across two Federal Government Administrations) with negative social profitability, with losses valued at more than 550 million dollars, placing it in the most problematic quartile of train projects in the world.

cost-benefit analysis ex-post evaluation public investment transport projects Latin America análisis costo-beneficio evaluación ex-post inversión pública proyectos de transporte América Latina CIENCIAS SOCIALES CIENCIAS SOCIALES

Point placement of late vegetative stage nitrogen splits increase the productivity, N-use efficiency and profitability of tropical maize under decade long conservation agriculture

Hari Sankar Nayak C.M. Parihar Shankar Lal Jat ML JAT Ahmed Abdallah (2022, [Artículo])

Non-Linear Growth Model Nitrogen Remobilization Right Placement Precision Nitrogen Management CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GROWTH MODELS NITROGEN NUTRIENT MANAGEMENT

Algorithmic differentiation of linear mixed models with variance-covariance structures

Fernando Henrique Toledo Jose Crossa Juan Burgueño Keith Gardner Rosa Angela Pacheco Gil (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MATHEMATICAL MODELS ALGORITHMS LINEAR MODELS

Alternative cropping and feeding options to enhance sustainability of mixed crop-livestock farms in Bangladesh

Timothy Joseph Krupnik Jeroen Groot (2024, [Artículo])

We investigated alternative cropping and feeding options for large (>10 cows), medium (5–10 cows) and small (≤4 cows) mixed crop – livestock farm types, to enhance economic and environmental performance in Jhenaidha and Meherpur districts – locations with increasing dairy production – in south western Bangladesh. Following focus group discussions with farmers on constraints and opportunities, we collected baseline data from one representative farm from each farm size class per district (six in total) to parameterize the whole-farm model FarmDESIGN. The six modelled farms were subjected to Pareto-based multi-objective (differential evolution algorithm) optimization to generate alternative dairy farm and fodder configurations. The objectives were to maximize farm profit, soil organic matter balance, and feed self-reliance, in addition to minimizing feed costs and soil nitrogen losses as indicators of sustainability. The cropped areas of the six baseline farms ranged from 0.6 to 4.0 ha and milk production per cow was between 1,640 and 3,560 kg year−1. Feed self-reliance was low (17%–57%) and soil N losses were high (74–342 kg ha−1 year−1). Subsequent trade-off analysis showed that increasing profit and soil organic matter balance was associated with higher risks of N losses. However, we found opportunities to improve economic and environmental performance simultaneously. Feed self-reliance could be increased by intensifying cropping and substituting fallow periods with appropriate fodder crops. For the farm type with the largest opportunity space and room to manoeuvre, we identified four strategies. Three strategies could be economically and environmentally benign, showing different opportunities for farm development with locally available resources.

Ruminant Feed Pareto-Based Optimization Farm Bioeconomic Model CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RUMINANT FEEDING BIOECONOMIC MODELS MIXED CROPPING FARMS LIVESTOCK

Clonal diploid and autopolyploid breeding strategies to harness heterosis: insights from stochastic simulation

Marlee Labroo Jeffrey Endelman Dorcus Gemenet Christian Werner Robert Gaynor GIOVANNY COVARRUBIAS-PAZARAN (2023, [Artículo])

Reciprocal Recurrent Selection Clonal Diploids CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA DIPLOIDY BREEDING HETEROSIS STOCHASTIC MODELS