Review of Nationally Determined Contributions (NCD) of Colombia from the perspective of food systems

Tek Sapkota (2023, [Documento de trabajo])

Food is a vital component of Colombia's economy. The impact of climate change on agriculture and food security in the country is severe. The effects have resulted in decreased production and in the productivity of agricultural soil. Desertification processes are accelerating and intensifying. Colombia's government formally submitted its Nationally Determined Contribution (NDC) on December 29, 2020. This paper examines Colombia's NDC from the standpoint of the food system.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE CHANGE GREENHOUSE GAS EMISSIONS FOOD SYSTEMS LAND USE CHANGE AGRICULTURE POLICIES DATA ANALYSIS FOOD WASTES

Colaboración y co-creación de conocimiento para una agricultura sostenible

Jelle Van Loon (2021, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SUSTAINABLE AGRICULTURE AGRIFOOD SYSTEMS FOOD SECURITY CLIMATE CHANGE ADAPTATION

Climate change and agriculture: Adaptation strategies and mitigation opportunities for smallholder farmers in South Asia

Tek Sapkota (2022, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE CHANGE STRATEGIES CLIMATE CHANGE IMPACT FOOD SECURITY FOOD SYSTEMS CLIMATE-SMART AGRICULTURE SUSTAINABLE INTENSIFICATION CONSERVATION AGRICULTURE SMALLHOLDERS

Editorial: Social and institutional innovations for achieving sustainable agricultural transformation in the global south

Paswel Marenya Jeetendra Aryal Annet Mulema Dil Bahadur Rahut (2023, [Artículo])

Agrifood Systems Transformation Global South Institutional Innovations CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRIFOOD SYSTEMS GREEN REVOLUTION INNOVATION SUSTAINABLE DEVELOPMENT

Monitoreo con drones en gráficas con viento dinámico

Jovanni Manuel López Elisea (2024, [Tesis de maestría])

108 páginas. Maestría en Optimización.

Dada una gráfica completa no dirigida, se desea recorrer un subconjunto de sus aristas usando una flotilla de drones. Los drones tienen baterías limitadas que pueden recargarse al regresar a la base y, en principio, el tiempo para recorrer una arista está en función de la distancia entre sus vértices. Sin embargo, ante la presencia de viento el tiempo de recorrer una arista puede depender del sentido en el que se haga. La dificultad del problema aumenta si además la intensidad del viento puede variar de un instante a otro. En esta tesis se aborda el problema anteriormente descrito para el caso particular en el que los vértices son puntos en el plano, el impacto del viento en los tiempos de recorrido de las aristas está relativamente acotado y el subconjunto de las aristas a recorrer inducen un árbol que abarca todos los vértices excepto la base de los drones. Dado que los drones operan simultáneamente y pueden recorrer distintas partes de la gráfica de manera independiente, se desea minimizar el tiempo que emplea el dron con el recorrido más tardado. Esta tesis presenta un modelo matemático para resolver el problema de manera exacta, así como tres heurísticas diferentes para obtener buenas soluciones factibles. La primera de estas heurísticas transforma una solución sin viento y sin batería en una solución con viento y batería. La segunda heurística es un algoritmo glotón sin comunicación entre los drones y la última heurística también es un algoritmo glotón, pero con comunicación entre los drones. Aunque el problema abordado resulta ser lo suficientemente difícil como para que su resolución exacta sea inviable en la práctica, las heurísticas diseñadas son fáciles de implementar y obtuvieron resultados razonables en un tiempo corto de cómputo.

Drone aircraft--Control systems. Drone aircraft--Mathematical models. Mathematical optimization. Heuristic programming. Dynamical systems. Graph theory. Micro vehículos aéreos. Optimización matemática. Programación heurística. Teoría de grafos. TL589.4 CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA MATEMÁTICAS INVESTIGACIÓN OPERATIVA DISTRIBUCIÓN Y TRANSPORTE

Research for development approaches in mixed crop-livestock systems of the Ethiopian highlands

Million Gebreyes James Hammond Lulseged Tamene Getachew Agegnehu Rabe Yahaya Anthony Whitbread (2023, [Artículo])

This study presents processes and success stories that emerged from Africa RISING's Research for Development project in the Ethiopian Highlands. The project has tested a combination of participatory tools at multiple levels, with systems thinking and concern for sustainable and diversified livelihoods. Bottom-up approaches guided the selection of technological interventions that could address the priority farming system challenges of the communities, leading to higher uptake levels and increased impact. Joint learning, appropriate technology selection, and the creation of an enabling environment such as the formation of farmer research groups, the establishment of innovation platforms, and capacity development for institutional and technical innovations were key to this study. The study concludes by identifying key lessons that focus more on matching innovations to community needs and geographies, systems orientation/integration of innovations, stepwise approaches to enhance the adoption of innovations, documenting farmers' capacity to modify innovations, building successful partnerships, and facilitating wider scaling of innovations for future implementation of agricultural research for development projects.

Action Research Systems Thinking CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA INNOVATION PARTNERSHIPS SCALING UP INTEGRATED CROP-LIVESTOCK SYSTEMS

Energy and economic efficiency of climate-smart agriculture practices in a rice–wheat cropping system of India

Manish Kakraliya Deepak Bijarniya Parbodh Chander Sharma ML JAT (2022, [Artículo])

Intensive Tillage Conventional Rice–Wheat Systems Energy Efficiency On-Farm Studies Climate-Smart Agricultural Practices CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE-SMART AGRICULTURE RICE WHEAT CROPPING SYSTEMS

Planting rice at monsoon onset could mitigate the impact of temperature stress on rice–wheat systems of Bihar, India

Carlo Montes Anton Urfels Eunjin Han Balwinder-Singh (2023, [Artículo])

Rainy Season TIMESAT APSIM Agricultural Production Systems Simulator Climate Adaptation CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RICE WHEAT MONSOONS WET SEASON CROP MODELLING CLIMATE CHANGE ADAPTATION

Wheat rust early warning systems in Ethiopia: implementation of remote sensing technologies to combat crop disease

Gerald Blasch (2020, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RUSTS MONITORING DISEASE SURVEILLANCE EARLY WARNING SYSTEMS REMOTE SENSING

High spatial resolution seasonal crop yield forecasting for heterogeneous maize environments in Oromia, Ethiopia

Kindie Tesfaye Vakhtang Shelia Pierre C. Sibiry Traore Dawit Solomon Gerrit Hoogenboom (2023, [Artículo])

Seasonal climate variability determines crop productivity in Ethiopia, where rainfed smallholder farming systems dominate in the agriculture production. Under such conditions, a functional and granular spatial yield forecasting system could provide risk management options for farmers and agricultural and policy experts, leading to greater economic and social benefits under highly variable environmental conditions. Yet, there are currently only a few forecasting systems to support early decision making for smallholder agriculture in developing countries such as Ethiopia. To address this challenge, a study was conducted to evaluate a seasonal crop yield forecast methodology implemented in the CCAFS Regional Agricultural Forecasting Toolbox (CRAFT). CRAFT is a software platform that can run pre-installed crop models and use the Climate Predictability Tool (CPT) to produce probabilistic crop yield forecasts with various lead times. Here we present data inputs, model calibration, evaluation, and yield forecast results, as well as limitations and assumptions made during forecasting maize yield. Simulations were conducted on a 0.083° or ∼ 10 km resolution grid using spatially variable soil, weather, maize hybrids, and crop management data as inputs for the Cropping System Model (CSM) of the Decision Support System for Agrotechnology Transfer (DSSAT). CRAFT combines gridded crop simulations and a multivariate statistical model to integrate the seasonal climate forecast for the crop yield forecasting. A statistical model was trained using 29 years (1991–2019) data on the Nino-3.4 Sea surface temperature anomalies (SSTA) as gridded predictors field and simulated maize yields as the predictand. After model calibration the regional aggregated hindcast simulation from 2015 to 2019 performed well (RMSE = 164 kg/ha). The yield forecasts in both the absolute and relative to the normal yield values were conducted for the 2020 season using different predictor fields and lead times from a grid cell to the national level. Yield forecast uncertainties were presented in terms of cumulative probability distributions. With reliable data and rigorous calibration, the study successfully demonstrated CRAFT's ability and applicability in forecasting maize yield for smallholder farming systems. Future studies should re-evaluate and address the importance of the size of agricultural areas while comparing aggregated simulated yields with yield data collected from a fraction of the target area.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CROP MODELLING DECISION SUPPORT SYSTEMS FORECASTING MAIZE