Production vulnerability to wheat blast disease under climate change

Diego Pequeno Jose Mauricio Fernandes Pawan Singh Willingthon Pavan Kai Sonder Richard Robertson Timothy Joseph Krupnik Olaf Erenstein Senthold Asseng (2024, [Artículo])

Wheat Blast Tropical Regions CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT PLANT DISEASES CLIMATE CHANGE PRODUCTION

Potential impacts of Ukraine-Russia armed conflict on global wheat food security: A quantitative exploration

Khondoker Mottaleb Gideon Kruseman Sieglinde Snapp (2022, [Artículo])

Violent conflict is a major cause of acute food crises. In 2021, at least 155 million people in 10 countries were severely food insecure and eight of those countries were experiencing armed conflict. On February 24, 2022, an armed conflict between Russian Federation (Russia) and Ukraine escalated. As Russia and Ukraine are major wheat exporters, this will aggravate the already precarious food security situation in many developing countries by disrupting wheat production and export and by accelerating price hikes in import-dependent developing countries. This study examines the potential impacts of this ongoing armed conflict between Russia and Ukraine on wheat price, consumption, and calorie intake from wheat. In doing so, it applies the conditional mixed process estimation procedure using information collected from 163 countries and territories for the years 2016–2019 from online database of the Food and Agriculture Organization of the United Nations (FAO). The study shows that, on average, a 1% decrease in the global wheat trade could increase the producers' price of wheat by 1.1%, and a 1% increase in the producers' price could reduce the yearly per capita wheat consumption by 0.59%, daily calorie intake by 0.54% and protein intake by 0.64% in the sampled countries. Based on this, the study demonstrates that a 50% reduction in wheat exports by Russia and Ukraine could increase the producers’ price of wheat by 15%, which would induce a reduction in wheat consumption and dietary energy intake by at least 8%. Since wheat export has reduced from both Russia and Ukraine, to avoid a food crisis in developing countries, policies are suggested, including near term improvement of domestic wheat production by promoting improved agronomic practices to close yield gaps to meet a substantial portion of wheat self-sufficiency goals. In the long run, countries in Africa, East Asia and South America can explore expanding wheat into new land area. International donor agencies can play a key role in supporting the ongoing wheat research and development activities.

Export-Import CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ARMED CONFLICTS CALORIES CONSUMPTION ELASTICITY FOOD SECURITY PRICES PRODUCTION WHEAT

Achieving wheat self-sufficiency in Brazil

Diego Pequeno Senthold Asseng (2024, [Artículo])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT SELF SUFFICIENCY PRODUCTION

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

Unsupervised segmentation and clustering time series approach to Southern Africa rainfall regime changes

Lovemore  Chipindu Walter Mupangwa Isaiah Nyagumbo Mainassara Zaman-Allah (2023, [Artículo])

Autoregressive Integrated Moving Average Facebook Prophet Hidden Markov Model Regression Regression with Hidden Logistic Process CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA COASTAL AREAS SEMIARID ZONES SUBHUMID ZONES RAINFALL CLIMATE CHANGE

Rainfall water quality at Atlixco, Puebla

Margarita Teutli Andrés Armando Sánchez Erendira Moreno Gutierrez (2021, [Artículo, Artículo])

This work reports the follow up of rainfall water quality at Atlixco, Puebla during the 2018 season. The main objective of this work was to evaluate how height and pollutants define the quality of water precipitated. Samples were collected at the roof of a house in Atlixco center, and others at the roof of a convent located at the San Miguel Hill, this represents a 44 m difference in height. Chemical composition was analyzed for 19 physicochemical parameters using gravimetric and spectrophotometric techniques. Obtained results were compared with drinking water standards finding that Pb and Cd contents are exceeded. Also, it was found a strong contribution of terrestrial sources since the marine rates are above unit, as well as excess concentrations whose values go from negative to positive. Finally, Pearson correlation was obtained finding that most of chemical parameters correlations are in disagreement for both sites, fact which confirms that ionic content is strongly influenced by anthropogenic sources.

rainfall ionic content marine rates excess concentrations Lluvia contenido iónico cociente marino concentración en exceso Estudios urbanos CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA

Agricultural emissions reduction potential by improving technical efficiency in crop production

Arun Khatri-Chhetri Tek Sapkota sofina maharjan Paresh Shirsath (2023, [Artículo])

CONTEXT: Global and national agricultural development policies normally tend to focus more on enhancing farm productivity through technological changes than on better use of existing technologies. The role of improving technical efficiency in greenhouse gas (GHG) emissions reduction from crop production is the least explored area in the agricultural sector. But improving technical efficiency is necessary in the context of the limited availability of existing natural resources (particularly land and water) and the need for GHG emission reduction from the agriculture sector. Technical efficiency gains in the production process are linked with the amount of input used nd the cost of production that determines both economic and environmental gains from the better use of existing technologies. OBJECTIVE: To assess a relationship between technical efficiency and GHG emissions and test the hypothesis that improving technical efficiency reduces GHG emissions from crop production. METHODS: This study used input-output data collected from 10,689 rice farms and 5220 wheat farms across India to estimate technical efficiency, global warming potential, and emission intensity (GHG emissions per unit of crop production) under the existing crop production practices. The GHG emissions from rice and wheat production were estimated using the CCAFS Mitigation Options Tool (CCAFS-MOT) and the technical efficiency of production was estimated through a stochastic production frontier analysis. RESULTS AND CONCLUSIONS: Results suggest that improving technical efficiency in crop production can reduce emission intensity but not necessarily total emissions. Moreover, our analysis does not support smallholders tend to be technically less efficient and the emissions per unit of food produced by smallholders can be relatively high. Alarge proportion of smallholders have high technical efficiency, less total GHG emissions, and low emissions intensity. This study indicates the levels of technical efficiency and GHG emission are largely influenced by farming typology, i.e. choice and use of existing technologies and management practices in crop cultivation. SIGNIFICANCE: This study will help to promote existing improved technologies targeting GHG emissions reduction from the agriculture production systems.

Technical Efficiency Interventions CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MITIGATION PRODUCTIVITY CROP PRODUCTION GREENHOUSE GAS EMISSIONS

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

Variabilidad estacional de la productividad primaria y su relación con la estratificación vertical en la bahía de La Paz, BCS

Seasonal variability of primary productivity and its relation to the vertical stratification in La Paz, BCS

RAFAEL CERVANTES DUARTE RUBEN ANTELMO MORALES PEREZ JOSE EDUARDO VALDEZ HOLGUIN (2003, [Artículo])

Se estimó la productividad primaria (pp) vía fluorescencia natural en la bahía de La Paz durante octubre de 1996, marzo, abril, mayo, junio, julio, septiembre, noviembre de 1997, y enero de 1998. simultáneamente a los registros de pp se realizaron perfiles verticales de temperatura (CTD) para determinar la estructura de la columna de agua, y de los cuales se estimó el índice de estratificación (_) de 0 a 100 m de profundidad. Durante primavera se observaron los valores promedio más altos de pp (16 mg C m-3 h-1) y valores bajos de _ (50 J m-3) que corresponden a una columna de agua casi homogénea. En contraste, en las subsecuentes estaciones (verano y otoño), la pp decreció a valores mínimos (2 y 5 mg C m-3 h-1), mientras que _ se incrementó significativamente (249 y 347 J m-3). Los resultados indican que existe una dependencia lineal de la pp y la mezcla en la columna de agua (r2 = 0.62, p<0.05) durante los meses de verano. Periodos con una alta estratificación tienden posiblemente a inhibir la fertilización de la capa superficial, mientras que una columna de agua bien mezclada facilita el proceso de fertilización. Por lo tanto, es evidente que en la Bahía de La Paz la estratificación asume un papel importante como mecanismo que controla la pp y explica parcialmente la variabilidad estacional observada en la bahía.

Materia orgánica Producción primaria La Paz Fluorescencia natural BIOLOGÍA Y QUÍMICA