Contracting in teams with network technologies

Giselle Labrador Badía (2020, [Tesis de maestría])

We develop a contracting model between the owner and the workers of a firm when production depends directly on a network of synergies among workers. We aim to answer how the owner of the firm uses the network structure to maximize profits. With this purpose, we analyze two contracting regimes: single wage and perfect discrimination. We find that individual network characteristics, as well as aggregate measures, affect profits and salaries. We also study the parameters for wich the incentives to discriminate and to account for the network structure are significant.

Externalities (Economics) -- Mathematical models. CIENCIAS SOCIALES CIENCIAS SOCIALES

Economic development, transport investment, and urbanization in Mexico: causality and effects

Vicente German-Soto Alexsandra De la Peña Flores Karina García (2023, [Artículo, Artículo])

The transport investment is often used as a tool for economic development and urbanization. However, there is still debate about whether transport improvements promote development and urbanization or, conversely, these latter create the conditions that stimulate the transport. In theory, the transport system contributes to development and urbanization because it speeds up the exchange of goods and services, but the effects can also be reversed, so the direction of causality is not so easily identified. This work uses Mexican state information of the 1988-2018 period, grouped as panel, to know both magnitude and direction of the impacts. Methodology consists in cointegration tests and VECM regressions. The results reveal that long-term causality goes from economic development to transport and its subsectors, which means that economic development is a necessary condition to modernize transport in Mexico. For urbanization, the causality and magnitude of the effects vary depending on the transport subsector. The total economy and passenger sector’s investments cause urbanization, but transportation and subsectors of cargo carriers and communications estimate two-way causality. The conclusions suggest that urbanization depends on improvements in transportation and the latter, in turn, on economic development.

Desarrollo regional Transporte Economía urbana Causalidad Modelo VECM CIENCIAS SOCIALES CIENCIAS SOCIALES Economic development, Granger-Causality, VECM Models, Productivity, Urban Economics

Informalidad laboral municipal en México: análisis de sus causas desde un enfoque espacial

Edison Smith Fonseca Correcha (2020, [Tesis de maestría])

Para el año 2019, más de 30 millones de trabajadores mexicanos estuvieron ejerciendo sus labores en condiciones informales, es decir, excluidos de la seguridad social. Para mitigar este problema público, las políticas públicas en diferentes niveles de gobierno han estado enfocadas principalmente en atacar dos de las posibles causas del problema: los incentivos económicos y la formación de la fuerza laboral. Con el fin de hacer una contribución sobre la relevancia de otras causas en la informalidad laboral, esta investigación presenta evidencia sobre el efecto que tienen los factores espaciales, sociodemográficos, de incentivos económicos y de estructura empresarial sobre la informalidad laboral municipal. Con base en los hallazgos, las recomendaciones de política pública se enfocan en aprovechar algunas estrategias de desarrollo económico regional para generar la conformación de aglomeraciones municipales de empleo formal.

Informal sector (Economics) -- Effect of space on -- Mexico -- Econometric models. Informal sector (Economics) -- Effect of demography on -- Mexico -- Econometric models. Informal sector (Economics) -- Effect of economic aspects on -- Mexico -- Econometric models. CIENCIAS SOCIALES CIENCIAS SOCIALES

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

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

Women, economic resilience, gender norms in a time of climate change: what do we know?

Cathy Farnworth Anne Rietveld Rachel Voss Angela Meentzen (2023, [Artículo])

This literature delves into 82 research articles, published between 2016 and 2022, to develop a deep understanding of how women manage their lives and livelihoods within their agrifood systems when these systems are being affected, sometimes devastatingly, by climate change. The Findings show that four core gender norms affect the ability of women to achieve economic resilience in the face of climate change operate in agrifood production systems. Each of these gender norms speaks to male privilege: (i) Men are primary decision-makers, (ii) Men are breadwinners, (iii) Men control assets, and (iv) Men are food system actors. These gender norms are widely held and challenge women’s abilities to become economically resilient. These norms are made more powerful still because they fuse with each other and act on multiple levels, and they serve to support other norms which limit women’s scope to act. It is particularly noteworthy that many institutional actors, ranging from community decision-makers to development partners, tend to reinforce rather than challenge gender norms because they do not critically review their own assumptions.

However, the four gender norms cited are not hegemonic. First, there is limited and intriguing evidence that intersectional identities can influence women’s resilience in significant ways. Second, gender norms governing women’s roles and power in agrifood systems are changing in response to climate change and other forces, with implications for how women respond to future climate shocks. Third, paying attention to local realities is important – behaviours do not necessarily substantiate local norms. Fourth, women experience strong support from other women in savings groups, religious organisations, reciprocal labour, and others. Fifth, critical moments, such as climate disasters, offer potentially pivotal moments of change which could permit women unusually high levels of agency to overcome restrictive gender norms without being negatively sanctioned. The article concludes with recommendations for further research.

Economic Resilience Intersectional Identities Women Groups Support CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ECONOMICS RESILIENCE CLIMATE CHANGE GENDER NORMS AGRIFOOD SYSTEMS WOMEN

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

Determinantes del uso de efectivo en México: análisis a través de la ENIF 2018

Juan Pablo Gómez Ayala (2021, [Tesis de maestría])

En este trabajo se analiza el impacto que tienen la economía formal como la educación y disciplina financiera en el uso de efectivo como medio de pago en México. Se estima que como mínimo, una persona que mejore su educación financiera (acerté una pregunta adicional) reducirá en 1.5% su probabilidad de comprar algún bien con efectivo; en cambio si mejora su disciplina financiera (practicar hábitos saludables más frecuentemente), aumentará la probabilidad de que pague servicios con efectivo en 2%.

Cash and carry transactions -- Payment -- Effect of financial literacy on -- Mexico -- 2018 -- Econometric models. Cash and carry transactions -- Payment -- Effect of informal sector (Economics) on -- Mexico -- 2018 -- Econometric models. CIENCIAS SOCIALES CIENCIAS SOCIALES

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