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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

Development and demographic parameters of Fall Armyworm (Spodoptera frugiperda J.E. Smith) when feeding on rice (Oryza sativa)

Timothy Joseph Krupnik (2023, [Artículo])

Fall Armyworm (FAW), Spodoptera frugiperda (Lepidoptera: Noctuidae), native to the Americas, is a polyphagous insect pest feeding on more than 350 plant species. We studied the developmental and demographic parameters of the maize (Zea mays) strain of FAW on rice (Oryza sativa), and compared the results with its prime host, maize. The developmental period from egg to adult among rice varieties did not differ significantly; however, it did differ significantly between rice and maize, as feeding on rice rather than maize extends development duration of FAW larvae by 15.15%. FAW larvae collected and reared on maize were found to be of significantly higher weight than those reared on rice at two sequential dates of their development; pupal weight however was observed as statistically similar between these two host crops. Regardless of the host, female adults always emerged before males; in maize, female FAW appeared 3.36 days earlier than males. Females derived from rice had longer pre-oviposition periods and shorter oviposition ones than those derived from maize. In rice and maize, the age-specific fecundity rate (mx) peaked at 40 days and 33 days, respectively. When the Fall Armyworm consumed maize instead of rice, there was an increase in the reproduction rate (R 0), the intrinsic rate of natural increase (rm), and the finite rate of increase (λ). For instance, when FAW fed on rice, the rm value was 0.121, whereas it rose to 0.173 when FAW fed on maize. Feeding on rice instead of maize resulted in significantly longer mean length of generation (tG) and doubling time (tD) for the fall armyworm (FAW). This suggests that it took a longer time for the FAW population to double when it was fed rice under controlled greenhouse conditions. In summary, our research suggests that FAW can survive and complete its life cycle on rice plants and on multiple varieties of rice in Bangladesh. However, field verification is necessary before drawing strong conclusions as to the risk posed by FAW in rice. This requires additional studies of FAW and associated insect community dynamics under non-controlled conditions and in the context of multi-species interactions in Asian rice fields.

Invasive Pest Life Table Parameters CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA HOST PLANTS PESTS RICE SPODOPTERA FRUGIPERDA FALL ARMYWORMS

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