Modeling the growth, yield and N dynamics of wheat for decoding the tillage and nitrogen nexus in 8-years long-term conservation agriculture based maize-wheat system

C.M. Parihar Dipaka Ranjan Sena Prakash Chand Ghasal Shankar Lal Jat Yashpal Singh Saharawat Mahesh Gathala Upendra Singh Hari Sankar Nayak (2024, [Artículo])

Context: Agricultural field experiments are costly and time-consuming, and their site-specific nature limits their ability to capture spatial and temporal variability. This hinders the transfer of crop management information across different locations, impeding effective agricultural decision-making. Further, accurate estimates of the benefits and risks of alternative crop and nutrient management options are crucial for effective decision-making in agriculture. Objective: The objective of this study was to utilize the Crop Environment Resource Synthesis CERES-Wheat model to simulate crop growth, yield, and nitrogen dynamics in a long-term conservation agriculture (CA) based wheat system. The study aimed to calibrate the model using data from a field experiment conducted during the 2019-20-2020-21 growing seasons and evaluation it with independent data from the year 2021–22. Method: Crop simulation models, such as the Crop Environment Resource Synthesis CERES-Wheat (DSSAT v 4.8), may provide valuable insights into crop growth and nitrogen dynamics, enabling decision makers to understand and manage production risk more effectively. Therefore, the present study employed the CERES-Wheat (DSSAT v 4.8) model and calibrated it using field data, including plant phenological phases, leaf area index, aboveground biomass, and grain yield from the 2019-20-2020-21 growing seasons. An independent dataset from the year 2021–22 was used for model evaluation. The model was used to investigate the relationship between growing degree days (GDD), temperature, nitrate and ammonical concentration in soil, and nitrogen uptake by the crop. Additionally, the study explored the impact of contrasting tillage practices and fertilizer nitrogen management options on wheat yields. The experimental site is situated at ICAR-Indian Agricultural Research Institute (IARI), New Delhi, representing Indian Trans-Gangetic Plains Zone (28o 40’N latitude, 77o 11’E longitude and an altitude of 228 m above sea level). The treatments consist of four nitrogen management options, viz., N0 (zero nitrogen), N150 (150 kg N ha−1 through urea), GS (Green seeker based urea application) and USG (urea super granules @150 kg N ha−1) in two contrasting tillage systems, i.e., CA-based zero tillage (ZT) and conventional tillage (CT). Result: The outcomes exhibited favorable agreement between the model’s simulations and the observed data for crop phenology (With less than 2 days variation in 50% onset of flowering), grain and biomass yield (Root mean square error; RMSE 336 kg ha−1 and 649 kg ha−1, respectively), and leaf area index (LAI) (RMSE 0.28 & normalized RMSE; nRMSE 6.69%). The model effectively captured the nitrate-N (NO3−-N) dynamics in the soil profile, exhibiting a remarkable concordance with observed data, as evident from its low RMSE = 12.39 kg ha−1 and nRMSE = 13.69%. Moreover, as it successfully simulated the N balance in the production system, the nitrate leaching and ammonia volatilization pattern as described by the model are highly useful to understand these critical phenomena under both conventional tillage (CT) and CA-based Zero Tillage (ZT) treatments. Conclusion: The study concludes that the DSSAT-CERES-Wheat model has significant potential to assess the impacts of tillage and nitrogen management practices on crop growth, yield, and soil nitrogen dynamics in the western Indo-Gangetic Plains (IGP) region. By providing reliable forecasts within the growing season, this modeling approach can facilitate better planning and more efficient resource management. Future implications: The successful implementation of the DSSAT-CERES-Wheat model in this study highlights its applicability in assessing crop performance and soil dynamics. Future research should focus on expanding the model’s capabilities by reducing its sensitivity to initial soil nitrogen levels to refine its predictions further. Moreover, the model’s integration with decision support systems and real-time data can enhance its usefulness in aiding agricultural decision-making and supporting sustainable crop management practices.

Nitrogen Dynamics Mechanistic Crop Growth Models Crop Simulation CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA NITROGEN CONSERVATION AGRICULTURE WHEAT MAIZE CROP GROWTH RATE SIMULATION MODELS

The impact of 1.5 °C and 2.0 °C global warming on global maize production and trade

Wei Xiong Tariq Ali (2022, [Artículo])

Future Climate Scenario Data Yield Reduction Risk CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE CHANGE GREENHOUSE EFFECT MAIZE MITIGATION SIMULATION ACCLIMATIZATION ADAPTATION GLOBAL WARMING

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

Simulación y validación de la reparación de ductos en servicio por la deposición directa de soldadura

LUIS DANTE MELENDEZ MORALES (2023, [Tesis de doctorado])

El transporte de hidrocarburos por ductos enterrados es la forma más segura, confiable y económica para su suministro, estos pueden extenderse grandes longitudes territoriales e inclusive atravesar países con tal de satisfacer la demanda energética. No obstante, los ductos pueden sufrir daños provocados por el ambiente, su operación o bien provocados por terceros, siendo necesario que sean intervenidos reemplazando las secciones dañadas. Las regulaciones nacionales y tratados internacionales desalientan la liberación de grandes cantidades de gas natural a la atmósfera, por demás de que un paro de suministro conlleva a desabasto energético, multas y a costosas operaciones asociadas con la rehabilitación del ducto, forzando a soldar envolventes y accesorios sin detener la operación de los ductos, esto se conoce como “soldadura en servicio”. La soldadura en servicio es un proceso tecnológico, por el cual se puede efectuar la interconexión y la reparación de ductos mientras están en operación, previo a realizar estas actividades, se requiere que dos riesgos sean evaluados: agrietamiento por hidrógeno y quemada pasante. Las simulaciones actuales y validaciones evalúan estos riesgos de forma independiente, pero debido a su interdependencia estos riesgos deben evaluarse en conjunto. Un método de reparación que no es normalmente empleado, pero con un alto potencial debido a su simplicidad y versatilidad, es la deposición directa de soldadura. En la presente investigación, se realizó una simulación numérica fluido-termo-mecánica acoplada con validación experimental, de la reparación de un tubo con flujo presurizado conteniendo un defecto interno por la deposición directa de soldadura. Por medio de la cual, es posible predecir el comportamiento estructural del ducto mientras se realiza la reparación.

La simulación numérica se efectuó con el apoyo del software ANSYS versión académica 22R2, siendo esta una herramienta de última generación capaz de contribuir en la predicción de mecanismos complejos como lo es la soldadura en servicio, incrementando con ello la seguridad y confiabilidad de estas operaciones. Cabe hacer mención, que la regulación nacional prohíbe la reparación de defectos internos por la deposición directa de soldadura, esto se debe principalmente a la falta de investigaciones validadas que respalden su viabilidad. Los resultados demostraron la efectividad de emplear este método de reparación para restaurar la resistencia mecánica de los ductos. Las inspecciones por pruebas no destructivas superficiales, subsuperficiales y volumétricas, evidenciaron que no ocurrió agrietamiento inmediatamente al finalizar la reparación y retardada (posterior a por lo menos 12 horas después de haberse finalizada la reparación, tiempo suficiente para permitir la difusión de hidrógeno atómico a hidrógeno molecular). Las curvas de tendencia de temperatura mostraron buena aproximación teniéndose una diferencia máxima de 5.09% entre los resultados numéricos y experimental. Los resultados numéricos y experimentales de la deformación perimetral a lo largo de la longitud de la tubería mostraron un comportamiento similar con una diferencia significativa del 17.7% entre los valores numéricos atribuidos a la falta de información de entrada para las propiedades de la soldadura. El análisis estructural efectuado en este estudio emplea la estimación del riesgo de quemada pasante bajo presión interna, determinado por la ocurrencia de abultamiento radial localizado. Los resultados numéricos indican que no ocurre deformación plástica relevante. Se hace una fuerte recomendación para que las evaluaciones de análisis térmico empleen la morfología actual del defecto y no solo consideren el espesor remanente del tubo. De acuerdo con la revisión bibliográfica realizada y recientemente publicada, este tipo de simulación numérica acoplada con validación experimental de la reparación de ductos en servicio por deposición directa de soldadura para la reparación de defectos internos contemplando la prevención de quemada pasante y agrietamiento por hidrógeno no ha sido realizada con anterioridad.

Hydrocarbon transportation by buried pipelines is the safest, most reliable, and economical way for its supply; these can extend long territorial distances and even cross countries with the purpose of satisfying the energy demand. However, the pipelines can suffer damages caused by their environment, their operation, or provoked by third parties, making necessary interventions to replace the damaged sections. National regulations and international agreements discourage the release of large quantities of natural gas into the atmosphere; moreover, a stop in its supply entails an energetic shortage, fines, and expensive operations associated with the pipeline rehabilitation, forcing to weld sleeves and fittings without stop the pipeline operation, this is known as “In-Service Welding”. In-Service welding is a technological process for which interconnection and repair of pipelines can be made while they are in operation; before making it, two main risks need to be assessed: hydrogen cracking and burn-through. Current simulations and validations assess these risks independently, but due to their interdependence, these risks need to be assessed in conjunction. A repair method not normally used but with high potential due to its simplicity and versatility is the direct deposition of the weld. In the present research, a fluid-thermo-mechanical coupled numerical simulation with experimental validation was done of a repair on a pipe with pressurized flow having an internal defect by direct deposition of the weld. It is possible predict the structural behavior of a pipeline while the reparation is performed.

The numerical simulation was done with the support of ANSYS software academic version 22R2, the latest generation tool able to contribute to the prediction of complex mechanisms, as is in-service welding, increasing the security and confidence of these operations. It is worth mentioning that national regulation forbids the reparation of internal defects for direct deposition of the weld; the main reason is the lack of validated investigations supporting its viability. The results demonstrated the effectiveness of using this repair method to restore the mechanical strength of pipelines. Surface, sub-surface, and volumetric non-destructive inspections evidenced no cracking immediately to finish the repair and delayed (after at least 12 hours of having finished the repair, time enough to allow the hydrogen diffusion from atomic hydrogen to molecular hydrogen). Temperature tendency curves showed good approximations, having a maximum difference of 5.09 % between numerical and experimental. Perimeter deformation along the pipe length between numerical and experimental results displayed a similar behavior with a significant difference of 17.7% against numerical values attributed to the lack of input data for weld properties. The structural analysis performed in this study used the approach of the risk of burn-through under internal pressure determined by the occurrence of localized radial bulging. Numerical results indicated no relevant plastic strain occurs. It is strongly recommended that thermal analysis assessments using the actual defect morphology be performed, not only considering the remaining thickness of the pipe. According to the bibliographic revision performed and recently published, this kind of coupled numerical simulation of in-service repair or pipelines by direct deposition for repairing internal defects considering the prevention of burn-through and hydrogen cracking has not been done.

Ducto Soldadura en servicio Quemada pasante Agrietamiento por hidrógeno Reparación de soldadura Simulación numérica Pipeline In-service welding Burn-through Hydrogen cracking Weld repair Numerical simulation INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS OTRAS ESPECIALIDADES TECNOLÓGICAS OTRAS ESPECIALIDADES TECNOLÓGICAS

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

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

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

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