Effect of Prosopis Farcta, Urtica Dioica and Disper root on Vegetative Growth, Nutrients Contents of cv. Shahreban Pomegranate Transplant

This research was conducted on pomegranate transplants in a lath house during the growth season of 2020 of college of Agricultural engineering science, university of Duhok, to investigate the impact of spraying of Prosopis Farcta, Urtica Dioica and Disper root with 0,100, and 200 mg.L-1 concentration, with 0,100, and 200 mg.L-1 concentration and with (0, 75, and 150 mg.L-1) concentration respectively, on vegetative growth, nutrients contents of pomegranate transplants. The collected data indicate that the spraying of Prosopis Farcta extract, Urtica Dioica and Disper root especially at 200, 200, and 150 mg.L-1 respectively resulting in a considerable increase in the majority of the examined parameters. At high concentrations, the interaction of three examined components resulted in the maximum values of branches number 14.57, transplant height 143.67 cm, leaves number 157.33, Chlorophyll 48.97, Roots number 10, Root length 42.67 cm, leaf nitrogen content 2.507 %, phosphorus 0.267 % and potassium 1.433 %.

Global Sensitivity Analysis for CERES-Rice Model under Different Cultivars and Specific-Stage Variations of Climate Parameters

Global sensitivity analysis (SA) has become an efficient way to identify the most influential parameters on model results. However, the effects of cultivar variation and specific-stage variations of climate conditions on model outputs still remain unclear. In this study, 30 indica hybrid rice cultivars were simulated in the CERES-Rice model; then the Sobol’ method was used to perform a global SA on 16 investigated parameters for three model outputs (anthesis day, maturity day, and yield). In addition, we also compared the differences in the sensitivity results under four specific-stage variations (vegetative phase, panicle-formation phase, ripening phase, and the whole growth season) of climate conditions. The results indicated that (1) parameter Tavg, G4, and P2O are the most influential parameters for all model outputs across cultivars during the whole growth season; (2) under the vegetative-phase variation of climate parameters; the variability of model outputs is mainly controlled by parameter P2O and Tavg; (3) under the panicle-formation-phase or ripening-phase variation of climate parameters, parameter P2O was the dominant variable for all model outputs; (4) parameter PORM had a considerable effect (the total sensitivity index, STi; STi>0.05) on yield regardless of the various specific-stage variations of the climate parameters. Findings obtained from this study will contribute to understanding the comprehensive effects of crop parameters on model outputs under different cultivars and specific-stage variations of climate conditions.

A daily basin-wide sea ice thickness retrieval methodology: Stefan's Law Integrated Conducted Energy (SLICE)

As changes to Earth’s polar climate accelerate, the need for robust, long–term sea ice thickness observation datasets for monitoring those changes and for verification of global climate models is clear. By coupling a recently developed algorithm for retrieving snow–ice interface temperature from passive microwave satellite data to a thermodynamic sea ice energy balance relation known as Stefan's Law, we have developed a new retrieval method for estimating thermodynamic sea ice thickness growth from space: Stefan’s Law Integrated Conducted Energy (SLICE). The advantages of the SLICE retrieval method include daily basin-wide coverage and a potential for use beginning in 1987. The method requires an initial condition at the beginning of the sea ice growth season in order to produce absolute sea ice thickness and cannot as yet capture dynamic sea ice thickness changes. Validation of the method against ten ice mass balance buoys using the ice mass balance buoy thickness as the initial condition show a mean correlation of 0.991 and a mean bias of 0.008 m over the course of an entire sea ice growth season. Estimated Arctic basin-wide sea ice thickness from SLICE for the sea ice growth seasons beginning between 2012 through 2019 capture a mean of 12.0 % less volumetric growth than a CryoSat-2 and Soil Moisture and Ocean Salinity (SMOS) merged sea ice thickness product (CS2SMOS) and a mean of 8.3 % more volumetric growth than the Pan-Arctic Ice–Ocean Modeling and Assimilation System (PIOMAS). The spatial distribution of the sea ice thickness differences between the retrieval results and those reference datasets show patterns consistent with expected sea ice thickness changes due to dynamic effects. This new retrieval method is a viable basis for a long–term sea ice thickness climatology, especially if dynamic effects can be captured through inclusion of an ice motion dataset.

The breeding of spring rapeseed and brown mustard for resistance to Fusarium blight

During the growth season, the plants of rapeseed (Brassica napus L.) and brown mustard (Brassica juncea L.) are affected by various diseases: Fusarium blight, Verticillium blight, Alternaria blight, and others. The cultivation of rapeseed and mustard varieties resistant to diseases is a cost-effective and environmentally safe way to protect plants from diseases. The aim of the work was to evaluate the new breeding material of spring rapeseed and brown mustard for resistance to Fusarium blight in the form of tracheomycotic wilting of plants to continue breeding work to develop varieties of these crops. In 2017-2020, we carried out a phytopathological evaluation of new breeding samples of rapeseed and brown mustard for resistance to Fusarium blight. As a result, we selected a valuable breeding material of spring rapeseed and brown mustard resistant to Fusarium blight infection, which also exceeds the standard by economic characters. The productivity of the best selected samples is higher than the varieties Tavrion and Nika by 0.13-0.59 and 0.18-0.28 t/ha, respectively, the oil content of seeds – by 0.2-2.0 and 1.2-2.1 %, respectively. These samples will be used as donors of Fusarium blight resistance in breeding work during development of new varieties of spring rapeseed and brown mustard in the central zone of Krasnodar region.

The models of soybean varieties adapted to dry conditions

The article presents the analysis of early-ripening and mid-ripening promising and cultivated soybean varieties that received a comprehensive evaluation under irrigation conditions over the past period (2016-2018) of breeding with the increase in atmospheric drought and dry hot wind days to 77 with a long-term average annual indicator of 47 days with a relative humidity of less than 30%. We developed the scientifically based model of highly productive soybean varieties (2021-2023) with a yield of 2.8-3.8 t/ha and the growth season of 105-122 days. The model is based on the established correlations of the main morphological and biological characteristics with the grain productivity of irrigated sowing. The model of soybean varieties reflects characteristics that ensure the responsiveness of plants to watering and resistance to prolonged manifestations of atmospheric drought: a long growth season, low linear plant growth, significant leaf surface area, photosynthetic potential, dry biomass yield, number of plants before harvesting, thousand-seed weight, long-term duration of the “flowering-beans filling” period. The model is complemented by the indicators of high protein and fat content in seeds, as well as their yield from a unit of harvesting area.

WAP-1D-VAR v1.0: development and evaluation of a one-dimensional variational data assimilation model for the marine ecosystem along the West Antarctic Peninsula

The West Antarctic Peninsula (WAP) is a rapidly warming region, with substantial ecological and biogeochemical responses to the observed change and variability for the past decades, revealed by multi-decadal observations from the Palmer Antarctica Long-Term Ecological Research (LTER) program. The wealth of these long-term observations provides an important resource for ecosystem modeling, but there has been a lack of focus on the development of numerical models that simulate time-evolving plankton dynamics over the austral growth season along the coastal WAP. Here, we introduce a one-dimensional variational data assimilation planktonic ecosystem model (i.e., the WAP-1D-VAR v1.0 model) equipped with a model parameter optimization scheme. We first demonstrate the modified and newly added model schemes to the pre-existing food web and biogeochemical components of the other ecosystem models that WAP-1D-VAR model was adapted from, including diagnostic sea-ice forcing and trophic interactions specific to the WAP region. We then present the results from model experiments where we assimilate 11 different data types from an example Palmer LTER growth season (October 2002–March 2003) directly related to corresponding model state variables and flows between these variables. The iterative data assimilation procedure reduces the misfits between observations and model results by 58 %, compared to before optimization, via an optimized set of 12 parameters out of a total of 72 free parameters. The optimized model results capture key WAP ecological features, such as blooms during seasonal sea-ice retreat, the lack of macronutrient limitation, and modeled variables and flows comparable to other studies in the WAP region, as well as several important ecosystem metrics. One exception is that the model slightly underestimates particle export flux, for which we discuss potential underlying reasons. The data assimilation scheme of the WAP-1D-VAR model enables the available observational data to constrain previously poorly understood processes, including the partitioning of primary production by different phytoplankton groups, the optimal chlorophyll-to-carbon ratio of the WAP phytoplankton community, and the partitioning of dissolved organic carbon pools with different lability. The WAP-1D-VAR model can be successfully employed to link the snapshots collected by the available data sets together to explain and understand the observed dynamics along the coastal WAP.

Variability and Trend Analysis of Temperature, Rainfall and Characteristics of Crop Growth Season in Eastern Zone of Tigray Region, Northern Ethiopia

To favour farmers and adjusting their farming practices, long term weather analyses is essential to determine future directions and making adjustments required to existing systems. The main purpose of this study was thus to analyze the variability and trends of climatic variables (temperature and rainfall) and characteristics of crop growth season in Eastern zone of Tigray region for the period of 1980–2009. Detail investigations were carried out using parametric (Linear regression) and non-parametric tests (Mankendall and Sen’s slope estimator). Moreover, homogeneity test was applied using a method developed by Van Belle and Hughes for the general trend analysis. Furthermore, the trend of rainfall end to characterize crop growth season using R-Instat and XLSTAT software. It was found that the general trend of monthly rainfall experienced an overall significant increasing trend. The seasonal rainfall experienced significantly increasing trend during the summer rainy season (June–September) whilst a significant decreasing trend occurred in the short rainy season (February–May). Likewise, the seasonal maximum temperature trends exhibited a significant increasing trend in all seasons whereas the minimum temperature showed inhomogeneous trend across seasons as well as stations. Despite significant increase of rainfall in summer season, the trend of growing season characteristics (onset, cessation, length of growing period and dry spell length) did not change significantly over the study period. However, the variability of rainfall and dry spell length was found to be very large. Hence, crop production in the study area demands appropriate adaptation strategies that considers the erratic nature of the rainfall, the long dry spell length in the season and increasing trends of temperature.

Review of Crop Yield Prediction using Machine Learning Techniques

Machine learning (ML) could be a helpful decision-making tool for predicting crop yields, in addition as for deciding what crops to plant and what to try throughout the crop's growth season. To help agricultural yield prediction studies, variety of machine learning techniques are used. I performed a literature review (LR) to extract and synthesize the algorithms and options employed in crop production prediction analysis. Temperature, rainfall, and soil types are most common measure used in the prediction as per my knowledge, whereas Artificial Neural Networks is the foremost normally used methodology in these models.

THE STUDY OF FIBER FLAX VARIETIES BRED IN THE TOMSK REGION IN THE SUBTAIGA ZONE OF THE OMSK REGION

We studied the fiber flax varieties bred in the Tomsk region in 2017–2019 in the subtaiga zone of the Omsk region on the experimental field of the Northern Agriculture Department of the Omsk Agricultural Scientific Center. We provide the material for 7 varieties. The soil of the experimental plot is gray wooded podzolic, medium, loamy with a humus content of 3–4 %. The predecessor is spring wheat after fallow. The research results showed that the weather conditions significantly affected the growth, development, and formation of the yield. In 2017, cold and wet weather generally affected the yield and quality of fiber flax in comparison to 2018-2019. As a result of the research, we established that for 3 years the best varieties were TOST 5 and Tomich. The variety TOST 5 gave a straw yield of 4.70 t/ha with number 1.9, an estimated fiber yield of 1.31 t/ha and a seed yield of 0.80 t/ha with the growth season of 61 days. The variety Tomich was characterized by the high quality of straw with number 2.1 with a short growth season (48 days), it had a straw yield of 4.0t/ha, a fiber yield of 1.18 t/ha and a seed yield of 0.70 t/ha. Therefore, the combination of varieties of different ripening duration will optimize the harvesting timing, raise the flax straw, and get a high yield of quality products.

THE STUDY OF THE OAT COLLECTION IN THE CONDITIONS OF THE SUBTAIGA ZONE OF THE OMSK REGION

We studied the collection of oat varieties in 2013–2015 in the conditions of the subtaiga zone of the Omsk region. The range was represented by genotypes of breeding of the Russian Federation (52.7 %), USA (16.2 %), Canada (7.2 %), Japan (5.4 %). The characteristics of the set are given according to the duration of the growth season, yield and elements of its structure, technological properties, and protein content. We determined the correlation between the indicators of grain quality and the parameters of the yield structure. We gave recommendations on the use of genetic sources to develop oat varieties with a high yield of grain and groats.