Climate smart bread wheat (Triticum aestivum L.) variety development for optimum moisture areas of Ethiopia

Bekele Abeyo Ayele Badebo Huluka (2023, [Artículo])

GGE Biplot AMMI Model CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SOFT WHEAT ENVIRONMENT GENOTYPES GRAIN YIELDS

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

Testing innovations for adoption of newer and more adapted maize varieties

Michael Ndegwa Pieter Rutsaert Jason Donovan Jordan Chamberlin (2023, [Objeto de congreso])

Changing Production Conditions Genetic Innovations Maize Hybrids CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA TESTING MAIZE VARIETIES YIELDS FARMERS EXPERIMENTATION

Enhancing maize yield in a conservation agriculture-based maize (Zea mays)- wheat (Triticum aestivum) system through efficient nitrogen management

C.M. Parihar Hari Sankar Nayak Dipaka Ranjan Sena Shankar Lal Jat Mahesh Gathala Upendra Singh (2023, [Artículo])

This study evaluated the impact of contrasting tillage and nitrogen management options on the growth, yield attributes, and yield of maize (Zea mays L.) in a conservation agriculture (CA)-based maize-wheat (Triticum aestivum L.) system. The field experiment was conducted during the rainy (kharif) seasons of 2020 and 2021 at the research farm of ICAR-Indian Agricultural Research Institute (IARI), New Delhi. The experiment was conducted in a split plot design with three tillage practices [conventional tillage with residue (CT), zero tillage with residue (ZT) and permanent beds with residue (PB)] as main plot treatments and in sub-plots five nitrogen management options [Control (without N fertilization), recommended dose of N @150 kg N/ha, Green Seeker-GS based application of split applied N, N applied as basal through urea super granules-USG + GS based application and 100% basal application of slow release fertilizer (SRF) @150 kg N/ha] with three replications. Results showed that both tillage and nitrogen management options had a significant impact on maize growth, yield attributes, and yield in both seasons. However, time to anthesis and physiological maturity were not significantly affected. Yield attributes were highest in the permanent beds and zero tillage plots, with similar numbers of grains per cob (486.1 and 468.6). The highest leaf area index (LAI) at 60 DAP was observed in PB (5.79), followed by ZT(5.68) and the lowest was recorded in CT (5.25) plots. The highest grain yield (2-year mean basis) was recorded with permanent beds plots (5516 kg/ha), while the lowest

was observed with conventional tillage (4931 kg/ha). Therefore, the study highlights the importance of CA practices for improving maize growth and yield, and suggests that farmers can achieve better results through the adoption of CA-based permanent beds and use of USG as nitrogen management option.

Green Seeker Urea Super Granules CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE UREA YIELDS ZERO TILLAGE NITROGEN

Impact of manures and fertilizers on yield and soil properties in a rice-wheat cropping system

Alison Laing Akbar Hossain (2023, [Artículo])

The use of chemical fertilizers under a rice-wheat cropping system (RWCS) has led to the emergence of micronutrient deficiency and decreased crop productivity. Thus, the experiment was conducted with the aim that the use of organic amendments would sustain productivity and improve the soil nutrient status under RWCS. A three-year experiment was conducted with different organic manures i.e. no manure (M0), farmyard manure@15 t ha-1 (M1), poultry manure@6 t ha-1(M2), press mud@15 t ha-1(M3), rice straw compost@6 t ha-1(M4) along with different levels of the recommended dose of fertilizer (RDF) i.e. 0% (F1), 75% (F2 and 100% (F3 in a split-plot design with three replications and plot size of 6 m x 1.2 m. Laboratory-based analysis of different soil as well as plant parameters was done using standard methodologies. The use of manures considerably improved the crop yield, macronutrients viz. nitrogen, phosphorus, potassium and micronutrients such as zinc, iron, manganese and copper, uptake in both the crops because of nutrient release from decomposed organic matter. Additionally, the increase in fertilizer dose increased these parameters. The system productivity was maximum recorded under F3M1 (13,052 kg ha-1) and results were statistically identical with F3M2 and F3M3. The significant upsurge of macro and micro-nutrients in soil and its correlation with yield outcomes was also observed through the combined use of manures as well as fertilizers. This study concluded that the use of 100% RDF integrated with organic manures, particularly farmyard manure would be a beneficial resource for increased crop yield, soil nutrient status and system productivity in RWCS in different regions of India.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ORGANIC FERTILIZERS YIELDS SOIL PROPERTIES RICE WHEAT CROPPING SYSTEMS

Seasonal climate forecasts (SCFs) based risk management strategies: A case study of rainfed rice cultivation in India

Nand Lal Kushwaha Paresh Shirsath Dipaka Ranjan Sena (2022, [Artículo])

FResampler1 Seasonal Climate Forecasts Decision Support System for Agrotechnology Transfer CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE CHANGE DECISION SUPPORT SYSTEMS YIELDS RICE RISK MANAGEMENT

Forage yield and composition of Avena strigosa Schreb at different harvesting frecuencies and intensities

Edmundo García-Moya Juan Burgueño Rodolfo Ramírez-Valverde Luis Alberto Miranda-Romero (2022, [Artículo])

Saia Oats Sward Height Residual Foliage CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA HARVESTING FREQUENCY FOLIAGE AVENA NUDA YIELDS

On-farm assessment of yield and quality traits in durum wheat

Facundo Tabbita Iván Ortíz-Monasterios Francisco Javier Pinera-Chavez Maria Itria Ibba Carlos Guzman (2023, [Artículo])

BACKGROUND: Durum wheat is key source of calories and nutrients for many regions of the world. Demand for it is predicted to increase. Further efforts are therefore needed to develop new cultivars adapted to different future scenarios. Developing a novel cultivar takes, on average, 10 years and advanced lines are tested during the process, in general, under standardized conditions. Although evaluating candidate genotypes for commercial release under different on-farm conditions is a strategy that is strongly recommended, its application for durum wheat and particularly for quality traits has been limited. This study evaluated the grain yield and quality performance of eight different genotypes across five contrasting farmers’ fields over two seasons. Combining different analysis strategies, the most outstanding and stable genotypes were identified. RESULTS: The analyses revealed that some traits were mainly explained by the genotype effect (thousand kernel weight, flour sodium dodecyl sulfate sedimentation volume, and flour yellowness), others by the management practices (yield and grain protein content), and others (test weight) by the year effect. In general, yield showed the highest range of variation across genotypes, management practices, and years and test weight the narrowest range. Flour yellowness was the most stable trait across management conditions, while yield-related traits were the most unstable. We also determined the most representative and discriminative field conditions, which is a beneficial strategy when breeders are constrained in their ability to develop multi-environment experiments. CONCLUSIONS: We concluded that assessing genotypes in different farming systems is a valid and complementary strategy for on-station trials for determining the performance of future commercial cultivars in heterogeneous environments to improve the breeding process and resources.

Wheat Quality GGE Analysis Flour Yellowness CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA FLOURS WHEAT QUALITY YIELDS FIELD EXPERIMENTATION

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

Impact of adoption of heat-stress tolerant maize hybrid on yield and profitability: evidence from Terai region of Nepal

Atul Kulkarni Keshab Babu Koirala Pervez Zaidi (2023, [Artículo])

Inverse Probability Weighted Regression Heat Tolerant Maize Hybrid Partial Budget CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA HEAT STRESS HEAT TOLERANCE MAIZE HYBRIDS BUDGETS YIELDS