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The impact of climate change on the corn yield in Serbia

Petrović et al. 134 increased by 0.3°C to 0.7°C compared to the period 1986-2005 (Falloon et al., 2015). There is a tendency to stop global warming at 1.5°C instead of 2°C. In the study (Zampieri et al., 2019) found that changes of climatic factors in recent 10 years had detrimental affect to maize production and that the global warming will substantially increase the risk of maize production losses in most world regions. The objective is to strengthen the global response to climate change threats (IPCC, 2018), and the objective can be reached only by the implementation of effective leadership. Human activities that directly or indirectly affect the climate are referred to as anthropogenic factors (http://www.sepa.gov.rs/download/1_web.pdf). Many climate change reports indicate that it will negatively affect agricultural production and on reducing the yield of varieties and hybrids (Luhunga, 2017) as well as the provision of food for the population. .One of the primary challenges of the 21st century is reducing food security risks from climate change consequences, as well as urgent adaptation actions to reduce risk. Increasing food production by 60% by 2050 to meet future consumption trends will also increase greenhouse gas emissions from the agricultural sector. Scenarios of limited global warming by 2°C related to the preindustrial level to the year 2100 indicate that agricultural production must be reduced. In this regard, society faces the challenges of adaptation and mitigation to provide food (Campbell et al., 2016). At 2 °C warming maize areas will be affected by heat stress and drought never experienced before, affecting many major and minor production regions. Sustainability of agricultural production is based on the cultivation of better-adapted varieties and hybrids and the application of appropriate cultivation technology (Knežević et al., 2015), which would reduce their sensitivity to changes in ecological conditions and generally to climate change (Ummenhofer et al., 2015). The application of this concept would significantly contribute to successful agricultural production to millions of producers in rural areas (Ceccarelli et al., 2010). Changes most often have adverse outcomes, and many agricultural systems become less productive, with economic and social consequences that affect the security of food supplying of the population (FAO, 2016). In Serbia, the energy sector provides the highest percentage of greenhouse gas emissions, around 78%, followed by the agricultural sector with 14.6% (Ministry of Agriculture and Environmental Protection, 2014). In order to avoid the dangers of climate change in the future, many countries and regions have set targets for reducing greenhouse gas emissions (ElRamady et al., 2012). Serbia was among the first countries in the Western Balkans region to adopt the NDC (Nationally Determined Contribution), with a defined target of 9.8% for the emission of harmful gases reduction by 2030 compared to emission in 1990 (Jovović and Jovičić, 2017). Agriculture is traditionally one of the essential branches of the economy in Serbia. It employs a large number of people (10%), either directly or indirectly, contributing significantly to the country's income, as it is one of the key components of economic development. It accounts for 9.5% of national GDP (2013) (Ministry of Environmental Protection, 2017). If the food industry is included in the agriculture share of GDP, its contribution is about 40%. Agriculture estimates for 26% of exports and consequently significantly contributes to our country's income growth (Ministry of Environmental Protection, 2010). As agriculture is vital for our country's economy and the people in rural areas, an approach to climate change adaptation is fundamental. Both rural and urban populations are highly dependent on this branch of the economy. Adapting to the negative impacts of climate change is essential to food security fostering. The study aims to examine the existence of a correlation between selected climatic factors, which represent predictor variables and corn yield, which is selected as criterion variables. The core goal of the research is to analyze the impact of climatic factors through selected parameters (maximum temperature, minimum temperature, precipitation quantity, relative humidity and sunshine) on a particular agricultural crop (corn). 2. Data and methodology Areas of Serbia were included in the research, which analyzes three regions: West Bačka District (Sombor, Kula, Apatin, Odžaci), Šumadija District (Kragujevac, Aranđelovac, Topola, Batočina, Lapovo, Knić, and Rača) and Nišava District (Niš, Aleksinac, Doljevac, Merošina, Ražanj, Gadžin Han and Svrljig). Data on Climate conditions values for the research period from 1991 to 2011 are obtained using data from the Republic Hydrometeorological Institute of Serbia. Observed annual values of the state of the conditions of climate (maximum and minimum air temperature, precipitation quantity, humidity, and sunshine) processed on an annual basis in the main meteorological stations that exist at selected locations. The research studied corn, as the most common field plant in Serbia. Data on the annual yield of selected agricultural crops, expressed in tons (t) and kg ha-1, as well as on sown areas (ha) were obtained using data from the Statistical Office of the Republic of Serbia. All data were processed using the SPSS Statistics 25 software package. This article has two starting hypotheses: null hypothesis H0 and alternative hypothesis H1. The null hypothesis Hо has been formulated negatively, arguing the opposite of the alternative hypothesis, and it states: There is no correlation between the dependent variable (crop yield) and the independent variable (conditions of climate). Alternative Hypothesis H1 states: There is a correlation between the dependent variable (crop yield) and the independent variable (conditions of climate). Hypothesis testing is based on one sample that is a dependent variable, and that is the annual corn yield in the Šumadija district (kg ha-1), while the independent variable is represented by climatic factors (maximum and minimum temperature, precipitation quantity, humidity, sunshine). For correlation analysis of crop yield and conditions of climate Pearson's correlation coefficient was applied. 3. Results and discussion Using the linear regression model, the null hypothesis N0 was tested. The obtained results show that the coefficient (R) of multiple regressions is 0.773 and represents a credible prediction interval. The Acta Agriculturae Serbica, 25 (50), 133‒140, 2020 135 coefficient of determination (R Square) is 0.598 or 59.8% of the variability of the dependent variable, which can be explained by the independent variable. The adjusted value of the coefficient of determination (Adjusted R Square) represents a less biased assessment of the coefficient of determination (Table 1). Table 1. Indicators of regression analysis for correlation of conditions of climate and crop yields Model Summary Model R R Square Adjusted R Square 1 0.773a 0.598 0.465 a. Predictors: (Constant): Sunshine, T minimum, T maximum, Humidity, Precipitation quantity. Author's own calculations The values presented in Table 2 indicate that if the probability is less than 0.05 (0.011<0.05), the null hypothesis H0 is rejected, which means that it makes sense to use the selected model to explain the correlation between crop yields and conditions of climate. In the following research, the degree of correlation and their statistical significance in the three selected locations will be determined using Pearson's correlation coefficient. Table 2. Indicators of regression analysis for correlation of conditions of climate and crop yields ANOVAa Model Sum of Squares df Mean Square F Sig. Regression 16980318.40 5 3396063.680 4.467 0.011b Residual 11402896.55 15 760193.103 Total 28383214.95 20 a. Dependent Variable: Corn yield (kg ha-1) b. Predictors: (Constant): Sunshine, T minimum, T maximum, Humidity, Precipitation quantity. Author's own calculations Descriptive statistics are used in the initial phase of the research. This type of analysis was performed for temperature, precipitation, humidity, and insolation for the meteorological stations Sombor, Kragujevac, and Niš. The paper examines corn yield (kg ha-1), cultivated area (ha), and production (t) in the selected locations. Climate change has a substantial impact on agricultural production and the cultivation of all major crops. Variables that directly affect agricultural production are the rapid rise in mean temperatures and the increasing frequency and intensity of extreme weather events (Taranto et al., 2018). The methodology of descriptive statistics (Table 3) shows the state of selected conditions of climate on an annual basis for the meteorological stations Sombor, Kragujevac, and Niš for the period from 1991 to 2011. In the observed period, the highest value of the maximum temperature was 20.0 °C (Niš), 19.8 °C (Kragujevac) and 19.4 °C (Sombor). The minimum values of maximum temperature were 15.5°C (Sombor), 15.9 °C (Kragujevac) and 16.4 °C (Niš). Minimum values of minimum annual temperatures ranged from 5.3 °C to 5.8 °C (Sombor 5.3 °C, Kragujevac 5.5 °C and Niš 5.8 °C), while the highest values of minimum annual temperatures were between 7.2 °C 8.0 °C (Sombor 7.2 °C, Kragujevac 7.6 °C and Niš 8.0 °C). The data shows higher deviations at maximum temperatures (between its minimum and maximum values), and they are in a range from 3.6 °C to 3.9 °C compared to deviations at minimum temperatures (between its minimum and maximum values), where deviations are from 1.9 °C to 2.2 °C. The average maximum temperature ranges from 17.1 °C (Sombor) to 18.4 °C (Niš). At minimum annual temperatures, mean values range from 6.3 °C (Sombor) to 6.9 °C (Niš). Standard deviation values confirm more significant deviations at maximum temperatures (0.900.99) than at minimum temperatures (0.57-0.64). Estimating annual precipitation quantity in the selected period (1991-2011), notable deviations were observed in the minimum (277.5 mm) and maximum (1035.6 mm) annual precipitation values recorded at the Sombor meteorological station. Due to the more significant difference in precipitation quantity in the aforementioned meteorological station compared to the other two meteorological stations (Niš and Kragujevac), the highest deviation from the mean value of 163.4 mm and the highest average annual precipitation quantity of 633.3 mm are also observed. The lowest humidity values were recorded at the southernmost meteorological station (Niš), compared to the previous two meteorological stations (Kragujevac, Sombor). For a selected period of observation (1991-2011), the most hours of sunshine was registered at the Sombor meteorological station (2127.6 h). The highest deviation between the minimum annual (1745.9 h) and maximum annual (2399.6 h) hours of sunshine was recorded in the meteorological station Niš, and therefore, the standard deviation value (156.2) is also the largest. Approximately similar values of minimum and maximum annual values of sunshine were recorded at meteorological stations in Sombor and Kragujevac. The total increase of average annual temperature for Serbia due to climate change in the last century was 0.6°C, which does not seem like a significant change. The effects of climatic conditions on agriculture exist, which are registered in the comparison of yields during Petrović et al. 106 perennial production. It is essential to prepare adaptation measures to such conditions (Taranto et al., 2018). Table 3. Conditions of climate in meteorological stations Sombor, Kragujevac and Niš in the period from 1991 to 2011 Author's own calculations Multiple climatic variables, such as temperature, precipitation quantity, sunshine, and relative humidity, can affect crop yields, so the assumption is that there is a correlation between selected conditions of climate and selected agricultural crop (corn) for the analysis in the period from 1991 to 2011. The correlation between annual values of the conditions of climate and corn yield (kg ha-1) is different in locations selected for the research process (Table 4). Table 4. Correlation between annual values of conditions of climate and agricultural crops Serbia at three different locations from 1991 to 2011 Agricultural crop Corn (kg ha-1) Conditions of climate Maximum Temp. (°С) Minimum Temp. (°С) Precipitation quantity (mm) Humidity (%) Sunshine (h) West Bačka District Correlation Coefficient -0.550** 0.059 0.400 0.331 -0.444* Sig. (2-tailed) 0.010 0.799 0.073 0.143 0.044 N 21 21 21 21 21 Šumadija District Correlation Coefficient -0.427 -0.216 0.001 0.201 -0.592** Sig. (2-tailed) 0.053 0.347 0.996 0.382 0.005 N 21 21 21 21 21 Nišava District Correlation Coefficient -0.235 0.126 0.624** 0.808** -0.564** Sig. (2-tailed) 0.305 0.585 0.003 0.000 0.010 N 21 21 21 21 20 **. Correlation is significant at the 0.01 level (2-tailed) *. Correlation is significant at the 0.05 level (2-tailed) Author's own calculations Corn growing is practically impossible in areas where the mean summer temperature is below 19 °C, or where the average nighttime temperature drops below 13 °C during the summer months. The average annual precipitation quantity should not be less than 250 mm (Bekavac, 2012). Corn is sown throughout Serbia. About 70% of the area is located in the lowlands up to 300 m above sea level, and the rest, about 30%, is located on mountainous terrain up to 700 m (Stojaković et al., 2010). In the West Bačka District, corn is grown at 77,017 ha and yields 453,662 tons, or 5,909 kg ha-1. There is a moderate negative correlation between corn and conditions of climate, moderate negative correlation between maximum temperature (r=-0.550) and sunshine (r=-0.444). The correlation for maximum Descriptive statistics – Conditions of climate Variable Meteorological station Mean Std. Deviation Minimum Maximum Range Variance Maximum temperature (°С) Kragujevac 17.8 0.9088 15.9 19.8 3.9 0.826 Sombor 17.1 0.9911 15.5 19.4 3.9 0.982 Niš 18.4 0.9788 16.4 20.0 3.6 0.958 Minimal Temperature (°С) Kragujevac 6.7 0.5750 5.5 7.6 2.1 0.331 Sombor 6.3 0.6361 5.3 7.2 1.9 0.405 Niš 6.9 0.6407 5.8 8.0 2.2 0.410 Precipitation quantity (mm) Kragujevac 626.6 135.8805 378.8 839.5 460.7 18,463.498 Sombor 633.3 163.4191 277.5 1,035.6 758.1 26,705.811 Niš 590.1 104.5827 385.6 756.3 370.7 10,937.532 Humidity (%) Kragujevac 72.3 3.903 66 86 20 15.233 Sombor 71.5 3.737 63 79 16 13.962 Niš 69.3 2.617 64 73 9 6.848 Sunshine (h) Kragujevac 2065.1 174.8304 1,803.5 2449.6 646.1 30,565.666 Sombor 2127.6 147.0386 1,871.8 2449.3 577.5 21,620.355 Niš 2002.9 156.2033 1,745.9 2399.6 653.7 24,399.456 Acta Agriculturae Serbica, 25 (50), 133‒140, 2020 137 temperature has no statistical significance (0.010 =0.01), while the correlation for sunshine has statistical significance (0.044<0.05). In the Šumadija District, corn is planted in an average area size of 40,252 ha, and yields of 152,278 tons or 3,919 kg ha-1. A moderate negative correlation is expressed for sunshine (r=0.592) and is statistically significant (0.005>0.01). In the Nišava District, corn is sown in average size areas of 30,024 ha. Yields are 100,438 tons or 3,470 kg ha-1. There is a positive correlation with precipitation quantity (r=0.624) and a very high positive correlation with humidity (r=0.808). The correlation is statistically significant (0.003<0.01 and 0.000<0.01). When it comes to the correlation with the sunshine, it has a moderate negative value but has no statistical significance (0.010=0.01). Weather characteristics, especially precipitation and temperature regimes, play an essential role in the establishment of corn yield. Drought in combination with high air temperatures is considered to be a vital factor limiting corn yields (Markulj, 2010; Paunović et al., 2011). Therefore, the state must engage more in addressing drought problems. In this regard, national drought control committees have been established in many surrounding countries. One gets the impression that in Serbia, the problem of drought is too easily forgotten, after one or two rainy seasons (Dodig, 2006). Figure 1. Realized corn yield kg ha-1) in West Bačka, Šumadija and Nišava District for the period 1991-2011 It is the most often considered that corn is drought-resistant and consume water economically. However, since it creates a huge vegetative mass, gives high yields, has an extended vegetation period, corn consumes large total amounts of water. In the absence of water, corn successfully overcomes drought, but gives lower yields, because plants consume more difficult accessible categories and forms of water from the soil (Pejić et al., 2009). Often the cause of reduced corn yield in our conditions is the lack of precipitation throughout the year, or the amount and distribution of precipitation during the vegetation period (Kovačević et al., 2010; Paunović et al., 2012). In the selected observation period, there was a sharp decline in yields in 2000 (West Bačka District 3,505 kg ha-1, Šumadija District 2,018 kg ha-1 and Nišava district 1,234 kg ha-1) and in 2007 (West Bačka District 4,699 kg ha-1, Šumadija District 1,868 kg ha-1 and Nišava District 1759 kg ha-1). Table 5. Monthly precipitation amount (mm) during the vegetation period (April-September) from 1991 to 2011 in Šumadija, Nišava and West Baška districts Observed period 1991-2011 April May June July August September Total Optimal value 50.0 75.0 90.0 100.0 95.0 80.0 490.0 Šumadija District 55.2 55.4 70.0 64.3 61.4 59.2 365.5 Nišava District 56.7 59.6 56.0 45.2 45.8 55.5 318.8 West Bačka District 43.2 57.2 74.4 78.0 55.0 61.5 369.3 Author's own calculations Our agroecological conditional-optimal needs of corn for water, at average air temperatures, are as follows: April – 50 mm, May 75 mm, June – 90 mm, July – 100 mm, August – 95 mm and September – 80 mm, the total for the vegetation period 490 mm (Jaramaz, 2015). When analyzing the period 19912011, it is noticed that the average precipitation quantity for April was above the optimal in the territory of Šumadija (55.2 mm) and Nišava district (56.7 mm), while in West Bačka district (43.2 mm) it was below the optimal value. During the following months (May-September), the average precipitation quantity values at the selected locations were below the optimal ones. The total amount of precipitation quantity during the vegetation period at the selected locations was also below the optimal value. During the vegetation period in the selected period (1991-2011), the lowest amount of precipitation quantity was at the location of the West Bačka district in 2000 (June 9.8 mm and August 5.5 mm), 2003 (April 7.8 mm) and Petrović et al. 138 2007 (April 0.5 mm). In the Šumadija district in the vegetation period of corn, the lowest amount of precipitation quantity was in 2003 (August 5.1 mm) and 2007 (April 3.6 mm), while in the Nišava district it was recorded in 1996 (July 5.5 mm), 2000 (August 6.5 mm), 2007 (July 7.7 mm) and 2011 (August 4.1 mm). If each year is separately analysed during the vegetation period of corn, it is noticed that in the territory of Šumadija district the amount of precipitation quantity was above the optimal, in 1999 (603.4 mm), 2001 (631.7 mm) and 2005 (509.6 mm) years. In the Nišava district, it was only in 2001 (493.2 mm), while in the West Bačka district it was in 2001 (535.6 mm) and 2005 (590.4 mm). The lowest amount of precipitation quantity during the vegetation period was recorded in the West Bačka district in 2000 (138.2 mm) and 2003 (177.9 mm). It must be emphasized that corn yields also depend on the location where they are grown, but in this case, in addition to the previously mentioned fact, a significant role in reducing yields had a very small amount of precipitation quantity in certain months during the growing season. Table 6. Mean monthly temperatures (C°) during the vegetation period (April-September) from 1991 to 2011 in Šumadija, Nišava and West Bačka district Observed period 1991-2011 April May June July August September Average for the growing season Optimal value according to Stepanov 15.0 18.0 20.0 23.0 23.0 18.0 20.0 Šumadija District 11.7 16.7 20.4 22.3 22.0 16.8 18.3 Nišava District 12.2 17.2 20.9 22.9 22.8 17.5 18.9 West Bačka District 11.8 17.3 20.7 22.2 21.8 16.4 18.3 Author's own calculations The most significant production of corn takes place in areas where the isotherms of the warmest month range from 21°C to 27 °C, and the period without frost last 120-180 days. It is clear that the reaction of a plant depends on a combination of specific stress factors, the intensity of each stress and the stage of plant development (Bekavac, 2012; Rejeb et al., 2014). During the dry year of 2000, the maximum values of temperatures in selected locations were in August (West Bačka district 32.3 °C, Šumadija district 32.4 °C and Nišava district 33.5 °C), while in 2007 the maximum values of temperatures were in the vegetation period in July (West Bačka district 31.5 °С, Šumadija district 33.3 °С and Nišava district 35.2 °С). Studies by other authors have shown that there is a negative impact of both high temperature and drought on the growth, development and reproduction of cereals and that it affects agricultural crop productivity (Ihsan et al., 2016; Zandalinas et al., 2018). The analysis of the mean air temperature (Table 6) for the observation period (1991-2011) shows that in April and May, the mean air temperatures at the selected locations were below the optimal value. During June, they are slightly above the optimal value, while during July and August, the values are around optimal value. In September, air temperatures are again below the optimal value. During the multi-year period at selected locations, it is noticed that April was the warmest in 2000 with the highest values of 15.1 °С in Šumadija district, 15.0 °С in Nišava district and 14.5 °С in West Bačka district. May was the warmest in 2003 (Šumadija district 19.9 °С, Nišava district 20.2 °С, West Bačka district 20.5 °С), while June was the warmest in 2003 (Šumadija district 23.3 °С, Nišava district 23.5 °С, West Bačka district 24.6 °С) and 2007 (Šumadija district 22.9 °С, Nišava district 23.6 °С, West Bačka district 22.3 °С). The highest values of mean July temperatures were in 1995. (Šumadija district 23.6 °С, Nišava district 24.0 °С, West Bačka district 23.9 °С) and 2007 (Šumadija district 24.8 °С, Nišava district 26.2 °С, West Bačka district 23.6 °С). The highest average air temperatures during August were in 1992 (Šumadija district 24.7 °С, Nišava district 25.6 °С, West Bačka district 24.8 °С), 2000 (Šumadija district 23.9 °С, Nišava district 24.6 °С, West Bačka district 23.9 °С) and 2003 (Šumadija district 24.6 °С, Nišava district 25.1 °С, West Bačka district 24.5 °С). September was the warmest in 1992. (Šumadija district 20.7 °С, Nišava district 21.7 °С, West Bačka district 21.7 °С) and 2011 (Šumadija district 20.2 °С, Nišava district 21.6 °С, West Bačka district 21.6 °С). Researches show that climate change will affect corn yield, in conditions without irrigation, where the yield can be reduced by 58% for the period until 2030, or up to 73% for the period until 2050. In the case of irrigation, a smaller decrease in yield could be expected, up to 15% for the period until 2030, or up to 25% for the period until 2050. According to estimates, changes in corn yields by 2100, without irrigation, range from -52% to -22%, ranging from northern and western to eastern and southern parts of Serbia (Ministry of Agriculture and Environmental Protection, 2015). Climate change izražene through temperature increase can have positive and negative effects on crop yield, it has been found that an increase in temperature will have a negative effect on cereals (Adams et al., 1998; Mićanović et al., 2017). The global climate change scenario shows that in 2100, corn yields will be lower from 20% to 45% and wheat yields from 5% to 50% (FAO, 2016). Corn yields are quite sensitive to the effects of climate change associated with high daily temperatures that accelerate the phenological phase Acta Agriculturae Serbica, 25 (50), 133‒140, 2020 139 transitions of corn, decreasing yields (Tebaldi and Lobell, 2018). Estimates of annual damage to agriculture due to temperature increases or extended periods of drought will be more costly than damages in other activities. One possible solution to counteract the effects of climate change is to look for agricultural crops and varieties that are adapted to highly variable, extreme climates (Kovačević et al., 2011; Korres et al., 2016; Luhunga, 2017). 4. Conclusion In Serbia, in the period from 1960 to 2012, the temperature increased by an average of 0.3°C per decade, the variation of which, especially in the period of vegetative development of corn, has an impact on yield, economy of agricultural production and food supply for the population. Agriculture production suffers from the negative impact of climate change, and at the same time, it is a source of emissions of gases that contribute to the emergence of climate change. Extreme weather events, especially droughts, more dry days and days with extreme temperatures, play a crucial role in the effects of climate change on the yields of the most important agricultural crops. At selected locations in Serbia, highly significant correlations were established between monthly temperatures, precipitation quantity, sunshine, humidity and corn yield, and they resulted in different yields. During the vegetation period, corn needs precipitation, which showed a positive and very strong correlation. Increased duration of sunshine (positive correlation) affected the growth of maximum temperatures with extremely high values (33-35°C), and this had a negative effect on the formation of corn yield during the growing season, which shows the correlation that had a negative trend. In contrast to air temperature, the correlation between precipitation and corn yield had a very positive trend. Although corn is drought tolerant, during the vegetation period (April-September), it needs a vast amount of water to form a vegetative mass. The lowest precipitation quantity at the selected locations was most often in 2000, 2003 and 2007, and the West Bačka district (138.2 mm and 177.9 mm) had a very small amount of precipitation compared to the other two locations. Extremely high temperatures and low precipitation quantity had a negative effect on corn yield, especially during 2000 and 2007, whose values were the lowest in the selected observation period. Rising temperatures, reduced availability of water for irrigation and floods will be the main limiting factors in maintaining and increasing crop productivity and represent predictions for future climate scenarios. Within the global strategy, a strategy for adapting to climate change must be developed and implemented in Serbia as well, especially in the agricultural sector. 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Introduction
One of the central issues facing the world is the risk of significant climate change, which poses a global environmental threat to all branches of the economy (Hong et al., 2019;Petrović et al., 2018a). Climate changes that directly affect agricultural production are the rapid increase of medium temperatures, and the frequent occurrence of extreme weather conditions will present significant difficulties in cultivating the land and providing food for the population (Taranto et al., 2018).
The global average temperature is increased by 0.85°C in a period from 1880 to 2012, and scientifically the increase in temperature is explained by the increased concentration of greenhouse gases (carbon dioxide, methane, nitrogen dioxide) (IPCC, 2015;Henderson et al., 2015;Petrović et al., 2018b). There is an expectation that the temperature will continue to rise, and in a period from 2019 to 2035 will be increased by 0.3°C to 0.7°C compared to the period 1986-2005 (Falloon et al., 2015). There is a tendency to stop global warming at 1.5°C instead of 2°C. In the study (Zampieri et al., 2019) found that changes of climatic factors in recent 10 years had detrimental affect to maize production and that the global warming will substantially increase the risk of maize production losses in most world regions. The objective is to strengthen the global response to climate change threats (IPCC, 2018), and the objective can be reached only by the implementation of effective leadership. Human activities that directly or indirectly affect the climate are referred to as anthropogenic factors (http://www.sepa.gov.rs/download/1_web.pdf).
Many climate change reports indicate that it will negatively affect agricultural production and on reducing the yield of varieties and hybrids (Luhunga, 2017) as well as the provision of food for the population. .One of the primary challenges of the 21st century is reducing food security risks from climate change consequences, as well as urgent adaptation actions to reduce risk. Increasing food production by 60% by 2050 to meet future consumption trends will also increase greenhouse gas emissions from the agricultural sector. Scenarios of limited global warming by 2°C related to the preindustrial level to the year 2100 indicate that agricultural production must be reduced. In this regard, society faces the challenges of adaptation and mitigation to provide food (Campbell et al., 2016). At 2 °C warming maize areas will be affected by heat stress and drought never experienced before, affecting many major and minor production regions. Sustainability of agricultural production is based on the cultivation of better-adapted varieties and hybrids and the application of appropriate cultivation technology , which would reduce their sensitivity to changes in ecological conditions and generally to climate change (Ummenhofer et al., 2015). The application of this concept would significantly contribute to successful agricultural production to millions of producers in rural areas (Ceccarelli et al., 2010). Changes most often have adverse outcomes, and many agricultural systems become less productive, with economic and social consequences that affect the security of food supplying of the population (FAO, 2016).
In Serbia, the energy sector provides the highest percentage of greenhouse gas emissions, around 78%, followed by the agricultural sector with 14.6% (Ministry of Agriculture and Environmental Protection, 2014). In order to avoid the dangers of climate change in the future, many countries and regions have set targets for reducing greenhouse gas emissions (El-Ramady et al., 2012). Serbia was among the first countries in the Western Balkans region to adopt the NDC (Nationally Determined Contribution), with a defined target of 9.8% for the emission of harmful gases reduction by 2030 compared to emission in 1990 (Jovović and Jovičić, 2017).
Agriculture is traditionally one of the essential branches of the economy in Serbia. It employs a large number of people (10%), either directly or indirectly, contributing significantly to the country's income, as it is one of the key components of economic development. It accounts for 9.5% of national GDP (2013) (Ministry of Environmental Protection, 2017). If the food industry is included in the agriculture share of GDP, its contribution is about 40%. Agriculture estimates for 26% of exports and consequently significantly contributes to our country's income growth (Ministry of Environmental Protection, 2010).
As agriculture is vital for our country's economy and the people in rural areas, an approach to climate change adaptation is fundamental. Both rural and urban populations are highly dependent on this branch of the economy. Adapting to the negative impacts of climate change is essential to food security fostering.
The study aims to examine the existence of a correlation between selected climatic factors, which represent predictor variables and corn yield, which is selected as criterion variables. The core goal of the research is to analyze the impact of climatic factors through selected parameters (maximum temperature, minimum temperature, precipitation quantity, relative humidity and sunshine) on a particular agricultural crop (corn).
Data on Climate conditions values for the research period from 1991 to 2011 are obtained using data from the Republic Hydrometeorological Institute of Serbia.
Observed annual values of the state of the conditions of climate (maximum and minimum air temperature, precipitation quantity, humidity, and sunshine) processed on an annual basis in the main meteorological stations that exist at selected locations.
The research studied corn, as the most common field plant in Serbia. Data on the annual yield of selected agricultural crops, expressed in tons (t) and kg ha -1 , as well as on sown areas (ha) were obtained using data from the Statistical Office of the Republic of Serbia. All data were processed using the SPSS Statistics 25 software package.
This article has two starting hypotheses: null hypothesis H0 and alternative hypothesis H1. The null hypothesis Hо has been formulated negatively, arguing the opposite of the alternative hypothesis, and it states: There is no correlation between the dependent variable (crop yield) and the independent variable (conditions of climate).
Alternative Hypothesis H1 states: There is a correlation between the dependent variable (crop yield) and the independent variable (conditions of climate).
Hypothesis testing is based on one sample that is a dependent variable, and that is the annual corn yield in the Šumadija district (kg ha -1 ), while the independent variable is represented by climatic factors (maximum and minimum temperature, precipitation quantity, humidity, sunshine).
For correlation analysis of crop yield and conditions of climate Pearson's correlation coefficient was applied.

Results and discussion
Using the linear regression model, the null hypothesis N0 was tested. The obtained results show that the coefficient (R) of multiple regressions is 0.773 and represents a credible prediction interval. The coefficient of determination (R Square) is 0.598 or 59.8% of the variability of the dependent variable, which can be explained by the independent variable. The adjusted value of the coefficient of determination (Adjusted R Square) represents a less biased assessment of the coefficient of determination (Table  1). The values presented in Table 2 indicate that if the probability is less than 0.05 (0.011<0.05), the null hypothesis H0 is rejected, which means that it makes sense to use the selected model to explain the correlation between crop yields and conditions of climate. In the following research, the degree of correlation and their statistical significance in the three selected locations will be determined using Pearson's correlation coefficient. Descriptive statistics are used in the initial phase of the research. This type of analysis was performed for temperature, precipitation, humidity, and insolation for the meteorological stations Sombor, Kragujevac, and Niš. The paper examines corn yield (kg ha -1 ), cultivated area (ha), and production (t) in the selected locations.
Climate change has a substantial impact on agricultural production and the cultivation of all major crops. Variables that directly affect agricultural production are the rapid rise in mean temperatures and the increasing frequency and intensity of extreme weather events (Taranto et al., 2018). The methodology of descriptive statistics (Table 3) shows the state of selected conditions of climate on an annual basis for the meteorological stations Sombor, Kragujevac, and Niš for the period from 1991 to 2011. In the observed period, the highest value of the maximum temperature was 20.0 °C (Niš), 19.8 °C (Kragujevac) and 19.4 °C (Sombor). The minimum values of maximum temperature were 15.5°C (Sombor), 15.9 °C (Kragujevac) and 16.4 °C (Niš). Minimum values of minimum annual temperatures ranged from 5.3 °C to 5.8 °C (Sombor 5.3 °C, Kragujevac 5.5 °C and Niš 5.8 °C), while the highest values of minimum annual temperatures were between 7.2 °C -8.0 °C (Sombor 7.2 °C, Kragujevac 7.6 °C and Niš 8.0 °C). The data shows higher deviations at maximum temperatures (between its minimum and maximum values), and they are in a range from 3.6 °C to 3.9 °C compared to deviations at minimum temperatures (between its minimum and maximum values), where deviations are from 1.9 °C to 2.2 °C. The average maximum temperature ranges from 17.1 °C (Sombor) to 18.4 °C (Niš). At minimum annual temperatures, mean values range from 6.3 °C (Sombor) to 6.9 °C (Niš). Standard deviation values confirm more significant deviations at maximum temperatures (0.90-0.99) than at minimum temperatures (0.57-0.64). Estimating annual precipitation quantity in the selected period , notable deviations were observed in the minimum (277.5 mm) and maximum (1035.6 mm) annual precipitation values recorded at the Sombor meteorological station. Due to the more significant difference in precipitation quantity in the aforementioned meteorological station compared to the other two meteorological stations (Niš and Kragujevac), the highest deviation from the mean value of 163.4 mm and the highest average annual precipitation quantity of 633.3 mm are also observed.
The lowest humidity values were recorded at the southernmost meteorological station (Niš), compared to the previous two meteorological stations (Kragujevac, Sombor). For a selected period of observation , the most hours of sunshine was registered at the Sombor meteorological station (2127.6 h). The highest deviation between the minimum annual (1745.9 h) and maximum annual (2399.6 h) hours of sunshine was recorded in the meteorological station Niš, and therefore, the standard deviation value (156.2) is also the largest.
Approximately similar values of minimum and maximum annual values of sunshine were recorded at meteorological stations in Sombor and Kragujevac. The total increase of average annual temperature for Serbia due to climate change in the last century was 0.6°C, which does not seem like a significant change.
The effects of climatic conditions on agriculture exist, which are registered in the comparison of yields during perennial production. It is essential to prepare adaptation measures to such conditions (Taranto et al., 2018). Multiple climatic variables, such as temperature, precipitation quantity, sunshine, and relative humidity, can affect crop yields, so the assumption is that there is a correlation between selected conditions of climate and selected agricultural crop (corn) for the analysis in the period from 1991 to 2011. The correlation between annual values of the conditions of climate and corn yield (kg ha -1 ) is different in locations selected for the research process (Table 4). Corn growing is practically impossible in areas where the mean summer temperature is below 19 °C, or where the average nighttime temperature drops below 13 °C during the summer months. The average annual precipitation quantity should not be less than 250 mm (Bekavac, 2012). Corn is sown throughout Serbia. About 70% of the area is located in the lowlands up to 300 m above sea level, and the rest, about 30%, is located on mountainous terrain up to 700 m (Stojaković et al., 2010).
In the West Bačka District, corn is grown at 77,017 ha and yields 453,662 tons, or 5,909 kg ha -1 . There is a moderate negative correlation between corn and conditions of climate, moderate negative correlation between maximum temperature (r=-0.550) and sunshine (r=-0.444 In the Nišava District, corn is sown in average size areas of 30,024 ha. Yields are 100,438 tons or 3,470 kg ha -1 . There is a positive correlation with precipitation quantity (r=0.624) and a very high positive correlation with humidity (r=0.808). The correlation is statistically significant (0.003<0.01 and 0.000<0.01). When it comes to the correlation with the sunshine, it has a moderate negative value but has no statistical significance (0.010=0.01).
Weather characteristics, especially precipitation and temperature regimes, play an essential role in the establishment of corn yield. Drought in combination with high air temperatures is considered to be a vital factor limiting corn yields (Markulj, 2010;. Therefore, the state must engage more in addressing drought problems. In this regard, national drought control committees have been established in many surrounding countries. One gets the impression that in Serbia, the problem of drought is too easily forgotten, after one or two rainy seasons (Dodig, 2006).

Figure 1. Realized corn yield kg ha -1 ) in West Bačka, Šumadija and Nišava District for the period 1991-2011
It is the most often considered that corn is drought-resistant and consume water economically. However, since it creates a huge vegetative mass, gives high yields, has an extended vegetation period, corn consumes large total amounts of water. In the absence of water, corn successfully overcomes drought, but gives lower yields, because plants consume more difficult accessible categories and forms of water from the soil (Pejić et al., 2009). Often the cause of reduced corn yield in our conditions is the lack of precipitation throughout the year, or the amount and distribution of precipitation during the vegetation period Paunović et al., 2012). In the selected observation period, there was a sharp decline in yields in 2000 (West Bačka District 3,505 kg ha -1 , Šumadija District 2,018 kg ha -1 and Nišava district 1,234 kg ha -1 ) and in 2007 (West Bačka District 4,699 kg ha -1 , Šumadija District 1,868 kg ha -1 and Nišava District 1759 kg ha -1 ). Our agroecological conditional-optimal needs of corn for water, at average air temperatures, are as follows: April -50 mm, May -75 mm, June -90 mm, July -100 mm, August -95 mm and September -80 mm, the total for the vegetation period 490 mm (Jaramaz, 2015). When analyzing the period 1991-2011, it is noticed that the average precipitation quantity for April was above the optimal in the territory of Šumadija (55.2 mm) and Nišava district (56.7 mm), while in West Bačka district (43.2 mm) it was below the optimal value. During the following months (May-September), the average precipitation quantity values at the selected locations were below the optimal ones. The total amount of precipitation quantity during the vegetation period at the selected locations was also below the optimal value. During the vegetation period in the selected period , the lowest amount of precipitation quantity was at the location of the West Bačka district in 2000 (June -9.8 mm and August -5.5 mm), 2003 (April -7.8 mm) and 2007 (April -0.5 mm). In the Šumadija district in the vegetation period of corn, the lowest amount of precipitation quantity was in 2003 (August -5.1 mm) and 2007 (April -3.6 mm), while in the Nišava district it was recorded in 1996 (July -5.5 mm), 2000 (August -6.5 mm), 2007 (July -7.7 mm) and 2011 (August -4.1 mm).
If each year is separately analysed during the vegetation period of corn, it is noticed that in the territory of Šumadija district the amount of precipitation quantity was above the optimal, in 1999 It must be emphasized that corn yields also depend on the location where they are grown, but in this case, in addition to the previously mentioned fact, a significant role in reducing yields had a very small amount of precipitation quantity in certain months during the growing season. The most significant production of corn takes place in areas where the isotherms of the warmest month range from 21°C to 27 °C, and the period without frost last 120-180 days. It is clear that the reaction of a plant depends on a combination of specific stress factors, the intensity of each stress and the stage of plant development (Bekavac, 2012;Rejeb et al., 2014). Studies by other authors have shown that there is a negative impact of both high temperature and drought on the growth, development and reproduction of cereals and that it affects agricultural crop productivity (Ihsan et al., 2016;Zandalinas et al., 2018).
The analysis of the mean air temperature (Table 6) for the observation period  shows that in April and May, the mean air temperatures at the selected locations were below the optimal value. During June, they are slightly above the optimal value, while during July and August, the values are around optimal value. In September, air temperatures are again below the optimal value.
During Researches show that climate change will affect corn yield, in conditions without irrigation, where the yield can be reduced by 58% for the period until 2030, or up to 73% for the period until 2050. In the case of irrigation, a smaller decrease in yield could be expected, up to 15% for the period until 2030, or up to 25% for the period until 2050. According to estimates, changes in corn yields by 2100, without irrigation, range from -52% to -22%, ranging from northern and western to eastern and southern parts of Serbia (Ministry of Agriculture and Environmental Protection, 2015).
Climate change izražene through temperature increase can have positive and negative effects on crop yield, it has been found that an increase in temperature will have a negative effect on cereals (Adams et al., 1998;Mićanović et al., 2017). The global climate change scenario shows that in 2100, corn yields will be lower from 20% to 45% and wheat yields from 5% to 50% (FAO, 2016). Corn yields are quite sensitive to the effects of climate change associated with high daily temperatures that accelerate the phenological phase transitions of corn, decreasing yields (Tebaldi and Lobell, 2018).
Estimates of annual damage to agriculture due to temperature increases or extended periods of drought will be more costly than damages in other activities. One possible solution to counteract the effects of climate change is to look for agricultural crops and varieties that are adapted to highly variable, extreme climates Korres et al., 2016;Luhunga, 2017).

Conclusion
In Serbia, in the period from 1960 to 2012, the temperature increased by an average of 0.3°C per decade, the variation of which, especially in the period of vegetative development of corn, has an impact on yield, economy of agricultural production and food supply for the population.
Agriculture production suffers from the negative impact of climate change, and at the same time, it is a source of emissions of gases that contribute to the emergence of climate change.
Extreme weather events, especially droughts, more dry days and days with extreme temperatures, play a crucial role in the effects of climate change on the yields of the most important agricultural crops.
At selected locations in Serbia, highly significant correlations were established between monthly temperatures, precipitation quantity, sunshine, humidity and corn yield, and they resulted in different yields.
During the vegetation period, corn needs precipitation, which showed a positive and very strong correlation. Increased duration of sunshine (positive correlation) affected the growth of maximum temperatures with extremely high values (33-35°C), and this had a negative effect on the formation of corn yield during the growing season, which shows the correlation that had a negative trend.
In contrast to air temperature, the correlation between precipitation and corn yield had a very positive trend. Although corn is drought tolerant, during the vegetation period (April-September), it needs a vast amount of water to form a vegetative mass. The lowest precipitation quantity at the selected locations was most often in 2000, 2003 and 2007, and the West Bačka district (138.2 mm and 177.9 mm) had a very small amount of precipitation compared to the other two locations.
Extremely high temperatures and low precipitation quantity had a negative effect on corn yield, especially during 2000 and 2007, whose values were the lowest in the selected observation period.
Rising temperatures, reduced availability of water for irrigation and floods will be the main limiting factors in maintaining and increasing crop productivity and represent predictions for future climate scenarios.
Within the global strategy, a strategy for adapting to climate change must be developed and implemented in Serbia as well, especially in the agricultural sector. It is necessary to create new varieties and hybrids adaptable to extreme climate conditions and to improve the technology of growing varieties and hybrids to mitigate the consequences of global warming (from drought, flood, frost). Also, it is necessary to limit the reduction of activities that contribute to the warming of the atmosphere and climatic factors changes.
The challenges ahead are sustainability and competitiveness in order to achieve targeted production and to meet growing food needs.


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Bacteria Isolated from the Aeration Chamber of Wastewater Treatment Plants Used in the Biocontrol and Promotion of Wheat Growth

: Background: Antagonisms against Fusarium spp. and multi-traits to protect and improve fertilization of wheat by bacterial strains from activated sludge were assessed. Methods: Isolated strains obtained were identiﬁed by 16S rRNA gene sequencing and the MALDI-TOF method, and their enzymatic proﬁle was investigated. Treated plant growth-promoting bacteria (PGPB) wheat kernels were grown in pots with soil contaminated with Fusarium conidia. Activated sludge is a collection of microorganisms exposed to strong environmental pressure (chemicals) and antagonistic properties. Results: The isolated bacterial strains were similar to: Ps-1 ( Serratia liquefaciens ), Ps-15 ( Serratia sp.) and Ps-9 ( Pseudomonas helleri ). The dual culture assay showed the highest antagonism of Ps-9 vs. Fusarium spp. The tested bacteria showed activity in the production of chitinase, a variety of proteases, enzymes that degrade various sugars, and esterase, which creates a complex that allows for a variety of strategies to control phytopathogens. The Ps-9 strain was able to solubilize phosphate. The Ps-9 and Ps-15 strains showed good ammoniﬁcation ability. A marked improvement was observed in test variants in pots inoculated with Fusarium spores after the use of Ps-9. The Ps-9 strain reduced the disease index to traces of symptoms of both species of Fusarium and increased the grain weight. Conclusions: The Ps-9 strain was proven to have high potential for application in the biocontrol and promotion of wheat growth.

Introduction
The use of plant growth-promoting bacteria (PGPB) is a highly promising method of integrated plant protection [1][2][3]. In the future, PGPB are expected to be used in support of chemical fertilizers, pesticides, and artificial growth regulators to help reduce doses and to decrease numerous side effects on sustainable agriculture. Among PGPB, the most numerous group are bacteria of the genera Acinetobacter, Pseudomonas (e.g., P. aeruginosa, P. aureofaciens, P. fluorescens, P. marginalis, P. putida), in addition to Bacillus (e.g., B. cereus, B. subtilis, B. coagulans, B. laterosporus, B. megaterium, B. mycoides, B. pasteurii, B. sphaericus), Enterobacter (E. agglomerans, E. cloacae), Azotobacter, Azospirillum, Burkholderia, Pantoea, Rhizobium, and Bradyrhizobium [3,4]. There is extensive published research indicating that bacteria have (at 150 rpm for 15 min). Subsequently, a decimal dilution of 1 mL of the tested samples in 9 mL of sterile saline (0.85% NaCl) solution was performed and aliquots of 0.1 mL of the sample were transferred into TSA (Tryptic Soy Agar Medium, Merck, Germany). All morphological types were collected and stored at −80 • C for future analyses.

Phylogenetic and MALDI-TOF Analysis
First, total DNA was extracted from pure culture using a Genomic Mini AX Bacteria Spin kit (A&A Biotechnology, Poland). Bacterial strains were identified based on the 16S rRNA nucleotide sequences obtained by using the primer pair 27F and 1492R designed by Lane [32] with PCR conditions as described in previous work [33]. Sequencing was performed at the company Genomed (Poland). All obtained sequences were aligned and consensus sequences with a length of 1000 bp were verified against the NCBI-GenBank database (National Center of Biotechnology Information, USA) using the BLAST tool. Next, the phylogenetic tree was constructed using the Neighbor-Joining method. The dendrogram included 16S rRNA sequences of the tested strains and other bacterial strains obtained from the Genbank database. For alignment and tree construction, the MEGA6 program was used. The sequences were deposited in the GenBank database under the following accession numbers: MK404730, MK404731, and MK404732.
The MALDI-TOF method was used to confirm the results of identification. Measurements were taken using a VitekMS MALDI-TOF (bioMérieux, Marcy l'Etoile, France) with an acceleration voltage of 200 kV, a mass range of 2-20 kDa, a laser frequency of 50 Hz, and an extraction delay time of 200 ns. All mass fingerprints were analyzed against the VitekMS V2.0 research use-only (RUO; SARAMIS version 4.13) databases (bioMérieux, Marcy l'Etoile, France). Isolates were identified in duplicate using the direct transfer protocol according to the manufacturer's recommendations. The isolates were briefly cultured for 48 h at 30 • C on TSA (Merck, Germany) and were then transferred to the target plate. One microliter of MALDI matrix VitekMS-CHCA was added to the spots. After crystallization of the matrix solution, the target was loaded into the MALDI-TOF MS system.

Antagonistic Test
The inhibition of pathogenic fungi by PGPB strains was analyzed with the method described by Przemieniecki et al. [33]. Mycelial discs with a diameter of 5 mm were cut from cultures of F. culmorum or F. solani and located on the center part of the PDA medium (BD Difco, USA) in Petri dishes. The analyzed bacteria were cultured on both sides next to disc, at a distance of 3 cm. Incubation was performed for 72 h at 28 • C (optimal for saprotrophic microorganisms), after which the percentage of inhibition was calculated as follows:

Enzymatic Activity
Biochemical parameters were evaluated in qualitative analyses using API ® 20NE and API ® ZYM kits (Biomérieux, France) according to the applicable instructions. Tests were incubated at the optimal temperature for saprotrophic bacteria.

Pot Experiment
Greenhouse tests were performed in round black plastic pots. These pots, of 28 cm diameter and 22 cm height, were filled with 8 kg of thoroughly mixed soils (loamy sand, pH KCl 6.0, total nitrogen −0.2%, P 2 O 5 −24.8 mg 100 g −1 of soil, K 2 O −20.1 mg 100 g −1 of soil, magnesium −4.8 mg 100 g −1 of soil). Prior to planting, soil water content was adjusted to a 60% field capacity in all pots. Before sowing, seeds were treated with 1% carboxymethyl cellulose (CMC) solution containing 5·10 8 CFU·mL −1 of the Ps-1, Ps-9, and Ps-15 strains. Seeds treated with sterile water were used as the experiment control (blank). Phytopathogens macroconidia (F. culmorum and F. solani) were obtained from a PDA medium (Potato Dextrose Agar, Difco, USA) pure culture by elution with sterile demineralized water. The reference strains were obtained from the UWM collection. The concentration of conidia was estimated by OD (optical density) measurement at a wavelength of 600 nm (Nanodrop 2000C, Thermo Fisher Scientific, Waltham, MA, USA). For treatments including the presence of phytopathogens, the 50 mL suspension of conidia was added to the pot cultivation layer of soil (~25 cm). Next, the soil was mixed and the final concentration of conidia in the fresh mass of soil was assessed as 10 5 g −1 . At the end of this process, the experimental soils samples were: (1) the control, without input of macroconidia; and soil contaminated with (2) F. culmorum and (3) F. solani macroconidia as negative controls. Twenty-four control seeds and seeds of spring wheat cv. Bombona treated with the bacteria strains Ps-1, Ps-9, and Ps-15 were sown in pots with experimental soils samples (1, 2, 3) at a depth of 1 cm. Each experimental variant contained 3 pots with 8 plants. Throughout the entire period of vegetation of the wheat, optimal humidity conditions were maintained, using the same quantity of irrigation in each pot. The cultivation was carried out in an unheated greenhouse that was maintained at as close as possible to field conditions. The spring wheat plants were harvested at the BBCH89 stage (full maturity of grain). Ear and stalk length were measured manually. Individual ears were cut. Roots were carefully washed to remove soil using tap water to reduce root loss and their length was measured manually. Excess moisture on the roots was blotted on bibulous papers before measuring the fresh weight of roots. Fresh weights of ears and roots were measured on an electronic analytical scale (Radwag, model AS 220.R2, [d]: 0.1 mg) at an ambient temperature of 21 • C. Then, the samples were dried in a laboratory oven (Memmert, model UNE 200-800) at a continuous temperature of 70 • C for 72 h until constant weight was reached. Dried ears and root samples were weighed once more and the percentage of dry matter was calculated (dry sample weight/fresh sample weight). Individual ears were threshed out and grain weights per ear were examined to determine yields.
The Disease Index (DI) was determined according to a 5-point scale, from 0 for no symptoms of the disease to 4 for the strongest symptoms, and then presented on a percentage scale. The results are expressed as a DI in percent, calculated based on the formula according Liu and Liu [34]: where: Σ (a·b)-the sum of the products of the degree of scale and the number of plants infected to this degree; n-total number of analyzed plants; i-the highest degree of scale.

Statistical Calculations
First, the normality of the distribution of the obtained results was tested (Shapiro-Wilk test). The antagonism potential (inhibitions zones) and biometry were calculated by ANOVA (p = 0.05) with the Duncan test in Statistica 12 (StatSoft, Tulsa, OK, USA). PCA (principal component analysis) and AHC (agglomerative hierarchical clustering) were performed in XLSTAT software (Addinsoft, https://www.xlstat.com/). AHC involving whole biometrical parameters was calculated using Bray and Curtis dissimilarity with the Ward method, while AHC used for the comparison of enzymatic properties was calculated by Euclidean distance based on UPGMA.

Identification of Analyzed Strains
The result of phylogenetic analysis is shown in Figure 1. Three strains of bacteria used in this study were identified (at species level) based on the 16S rRNA sequence for two of the three isolates. A small subunit of ribosomal sequences (16S) was 98% similar to Serratia liquefaciens for Ps-1, 98% similar to Pseudomonas helleri for Ps-9, and 99% similar to Serratia grimesii or S. quinivorans for Ps-15. The MALDI-TOF analyses confirmed phylogenetic results and taxonomic membership for all three strains.
Agronomy 2020, 10, x FOR PEER REVIEW 5 of 14 The MALDI-TOF analyses confirmed phylogenetic results and taxonomic membership for all three strains.

Antagonistic Test
The dual culture assay showed that the isolated PGPB strains are antagonists of the analyzed F. culmorum and F. solani species ( Table 1). The highest Fusarium inhibition was recorded for the Ps-9 strain ( Figure 2). The lowest inhibition was estimated for F. solani at 43% when treated with the Ps-1 strain ( Table 1). The results show that the tested bacterial strains have the potential to be effective biological control agents.

Antagonistic Test
The dual culture assay showed that the isolated PGPB strains are antagonists of the analyzed F. culmorum and F. solani species ( Table 1). The highest Fusarium inhibition was recorded for the Ps-9 strain ( Figure 2). The lowest inhibition was estimated for F. solani at 43% when treated with the Ps-1 strain ( Table 1). The results show that the tested bacterial strains have the potential to be effective biological control agents.

Enzymatic Activity
All three analyzed strains showed enzymatic activity for 15 tested enzymes and were identical in their qualitative appearance ( Table 2). The strains differed in the range of assimilation of simple compounds. The Ps-1 strain was able to use substrates, whereas for the other two strains there was a differing level of assimilation in two cases (Ps-1 used D-glucose and trisodium citrate to a lesser extent). Among the six analyzed metabolic traits, the Ps-9 strain was most active. It was also observed that all bacterial strains were characterized by the activity of: β-galactosidase; α-glucosidase; N-acetyl-β-glucosaminidase (chitinase); proteases (such as leucine, valine, and trypsin arylamidase); lipases and short-and long-chain esterases; and the ability to use sugars and simple compounds, such as D-mannose, D-mannitol, N-acetylglucosamine, D-maltose, and assimilation of potassium gluconate. The Ps-9 and Ps-15 strains demonstrated a good ability to utilize D-glucose. The Ps-1 strain showed a poor ability to assimilate D-glucose and L-arabinose. All strains were characterized by low β-glucosidase (cellulase) activity. The strains showed a high ability for the assimilation of decanoic and malic organic acids. It was also observed that the strains expressed high abilities of denitrification and glucose fermentation. The Ps-9 strain showed good phosphate dissolution capacity. Ps-9 and Ps-15 strains showed good ammonification ability and the Ps-1 strain had poor ammonification ability. None of the tested bacterial strains showed the ability to produce indole (Table 2).

Enzymatic Activity
All three analyzed strains showed enzymatic activity for 15 tested enzymes and were identical in their qualitative appearance ( Table 2). The strains differed in the range of assimilation of simple compounds. The Ps-1 strain was able to use substrates, whereas for the other two strains there was a differing level of assimilation in two cases (Ps-1 used D-glucose and trisodium citrate to a lesser extent). Among the six analyzed metabolic traits, the Ps-9 strain was most active. It was also observed that all bacterial strains were characterized by the activity of: β-galactosidase; α-glucosidase; N-acetyl-β-glucosaminidase (chitinase); proteases (such as leucine, valine, and trypsin arylamidase); lipases and short-and long-chain esterases; and the ability to use sugars and simple compounds, such as D-mannose, D-mannitol, N-acetylglucosamine, D-maltose, and assimilation of potassium gluconate. The Ps-9 and Ps-15 strains demonstrated a good ability to utilize D-glucose. The Ps-1 strain showed a poor ability to assimilate D-glucose and L-arabinose. All strains were characterized by low β-glucosidase (cellulase) activity. The strains showed a high ability for the assimilation of decanoic and malic organic acids. It was also observed that the strains expressed high abilities of denitrification and glucose fermentation. The Ps-9 strain showed good phosphate dissolution capacity. Ps-9 and Ps-15 strains showed good ammonification ability and the Ps-1 strain had poor ammonification ability. None of the tested bacterial strains showed the ability to produce indole ( Table 2). Table 2. Biochemical parameters of tested Ps-1, Ps-9, and Ps-15 bacteria strains.

Enzymatic activity
Alkaline phosphatase

Pot Experiment
The influence of the tested bacterial strains on the biometry and Disease Index (DI) of spring wheat is shown in Table 3. The grain yields varied. For both species of Fusarium, soil inoculation resulted in a significant decrease in the grain yield compared to the control. Additionally, the F. solani species had a significant harmful effect on all of the biometric parameters of plants, except for the dry mass of roots. The treatment of seeds with the Ps-9 strain provided effective protection against both F. culmorum and F. solani, as a result of which the grain yield was significantly higher than in the control. For the Ps-1 and Ps-15 strains, no significant improvement in grain yield was achieved as compared to the control, but the grain weight in these objects was greater than that of the plants grown in soil inoculated with phytopathogens (negative controls). Above all, however, bacterial inoculation showed high protective potential against diseases caused by fungi of the tested Fusarium genus. All strains of bacteria significantly reduced the occurrence of disease symptoms on the analyzed wheat plants, with the Ps-9 strains showing the greatest potential as an antagonist of phytopathogenic fungi of both species of Fusarium. Table 3. Biometry and Disease Index (DI) of spring wheat plants cultivated from seeds treated with the examined strains (Ps-1, Ps-9, and Ps-15) and in soil inoculated with F. culmorum and F. solani macroconidia. The calculated PCA (Figure 3) covered 70% of the total variability of the results, which shows the high power of the performed test. The results of the analysis indicate almost total separation of control objects (blank and negative) from objects treated with antagonistic bacteria. Variants without the use of any of the microorganisms showed a negative relation to the first and second coordinate axes, and were the closest to the Ps-15 + F. culmorum variant located around the center of the graph. Moreover, these objects were directly related to the largest stalk length values. In contrast, a group of proper control objects that were treated with F. culmorum or F. solani formed a pair of apparently different objects from the remainder of the results, which was accompanied by a closely related high DI index. Both Ps-1 and Ps-15 with F. solani variants showed a positive ratio towards both coordinate axes, and their location was mainly influenced by root dry mass, ear dry mass, root weight, and ear and root length, which indicates the direct impact of these beneficial bacteria on the improvement of these biometric parameters. However, the Ps-9 variants were similar to each other and relatively similar to the other objects using bacterial strain treatment. Their use had the greatest impact on ear weight, grain weight, and root length. In this analysis, a relationship between features was also observed, i.e., DI was almost completely negatively correlated with grain weight, root dry mass, and stalk weight. The other observed features were (at least partially) positively correlated with each other.
Agglomerative hierarchical clustering (AHC) analysis allowed the similarity in enzymatic activity between the analyzed groups of variants to be determined. It was observed that the strain Ps-9 differed clearly from Ps-1 and Ps-15 bacteria strains (Figure 4). Detailed analysis of the variants (PGPB bacteria × Fusarium spp.) allowed three different groups to be selected. Negative controls with both Fusarium pathogens formed one group, although they were nevertheless relatively similar to the group formed by the Ps-1 and Ps-15 PGPB strains and F. culmorum (Figure 4). Agglomerative hierarchical clustering (AHC) analysis allowed the similarity in enzymatic activity between the analyzed groups of variants to be determined. It was observed that the strain Ps-9 differed clearly from Ps-1 and Ps-15 bacteria strains (Figure 4). Detailed analysis of the variants (PGPB bacteria × Fusarium spp.) allowed three different groups to be selected. Negative controls with both Fusarium pathogens formed one group, although they were nevertheless relatively similar to the group formed by the Ps-1 and Ps-15 PGPB strains and F. culmorum (Figure 4).  Table 2); on the left: differences between  Agglomerative hierarchical clustering (AHC) analysis allowed the similarity in enzymatic activity between the analyzed groups of variants to be determined. It was observed that the strain Ps-9 differed clearly from Ps-1 and Ps-15 bacteria strains (Figure 4). Detailed analysis of the variants (PGPB bacteria × Fusarium spp.) allowed three different groups to be selected. Negative controls with both Fusarium pathogens formed one group, although they were nevertheless relatively similar to the group formed by the Ps-1 and Ps-15 PGPB strains and F. culmorum (Figure 4).  Table 2); on the left: differences between  Table 2); on the left: differences between variants used in pot tests (calculation based on results from Table 3). Abbreviations: Cont, C-control without the use of microorganisms; Fcul-F. culmorum; Fsol-F. solani; PS1, PS9, PS15-PGPB strains.

Discussions
In this study, the identified bacterial strains belonged to different species. The S. liquefaciens Ps-1 strain and the closely related Serratia sp. Ps-15 strain showed a high polymorphism of the 16S rRNA sequence with respect to the well-known S. marcescens and S. fonticola. A similar relationship was observed between the Pseudomonas helleri Ps-9 sequence and P. fluorescens, P. putida, and P. aeruginosa. Moreover, the strain used in the study formed its own unique clade and was very closely related to P. weihenstephanensis. It can be presumed that the activated sludge environment is a reservoir of new species of bacteria that can significantly affect the growth of plants, especially under the pressure of phytopathogenic fungi [1,3,5,6]. The identified bacteria belonged to the Pseudomonas and Serratia genera, which is in accordance with our previous results [35]. The review by Mahmood et al. [36] of the role of PGPB in seed biopriming points to Pseudomonas and Serratia as having the greatest potential as inducers of resistance to biotic stress. Our results indicate that Pseudomonas bacteria are more useful than Serratia bacteria in the control of Fusarium in wheat, but the antagonistic properties of the two Serratia strains were also different. The high potential of P. putida as a PGPB and biological agent has been demonstrated by Ahemad and Khan [37] and Przemieniecki et al. [38]. Furthermore, similarly to other species of Pseudomonas, the P. luteola SP0113 strain inhibited the growth of Fusarium sp. [39]. Fusarium play a significant role in agriculture because they adversely affect cereals by infecting the seedlings which leads to major economic losses in yields [40]. Indirect harmfulness manifested in the contamination of grain yields by mycotoxins produced by Fusarium is also of huge importance for the global economy and human health [17]. In the case of other crops and phytopathogens, the Serratia strain can show higher potential. Abuamsha et al. [41], who tested seed treatment with Serratia plymuthica HRO-C48 and Pseudomonas chlororaphis MA 342 against Leptosphaeria maculans, showed a reduction in blackleg disease symptoms in oilseed rape of about 72 and 54% respectively. This indicates the need for targeted PGPB selection depending on the crop and the purpose of protection.
When analyzing the results presented for metabolism diversity, a small variability of traits between strains was observed. The most important features are the ability to produce chitinase (N-acetyl-β-glucosaminidase), a variety of proteases and enzymes that break down bonds in various sugars, and the enzymes that hydrolyze phosphate monoesterase, lipolytic and esterase bonds, creating a complex of multi-trait properties that allows for a variety of strategies to fight against phytopathogenic fungi. The above features in combination with phosphate solubilization (Ps-9) and ammonification enable efficient decomposition and assimilation of organic matter, which results in the provision of nutrients to plants [6,8,16,42]. Przemieniecki et al. [39] have also shown the high enzymatic activity and assimilation efficiency of the Pseudomonas sp. SP0113 strain. The Pseudomonas sp. SP0113 strain demonstrated the ability of denitrification and ammonification, allowing nitrogen to be taken up by the plant root system. Nitrogen plays a key role in yield creation. It was shown that a PGPB, which participates in all changes and nitrogen fixation, has a positive effect on plant growth [43,44]. The observed activity of alkaline phosphatase, acid phosphatase and other enzymes involved in the metabolism of phosphorus showed that the analyzed Ps-9 strain also increased the availability of organic phosphorus for plant roots. The use of PGPB strains generally improved the majority of biometric parameters only in plants growing in soil with F. solani. In the case of F. culmorum, such an improvement was observed after the use of the Ps-9 strain and it was the only strain which almost completely reduced the rate of disease intensity in both objects of the experiment. In addition, the most important factor was the increase in the most important indicator, i.e., the grain weight. Data obtained on the basis of PCA proved to be of the greatest help in the interpretation of the results because it made it possible to determine the relationships between the variants and the observed features, which was also confirmed to a lesser extent with the use of post hoc analysis. There was no significant impact of the Ps-15 strain on variants infected with F. culmorum, whereas there was found to be an excellent improvement in biometric parameters with most strains and a direct link between the grain weight and DI, and thus the intensity of the plant disease state. An interesting, but not fully explained dependence was the result indicating that the higher the dry weight of the roots, the smaller the plant height. This indicates the uniqueness of the Ps-9 strain, which coincides overall with the best results in ANOVA and PCA analysis. A more detailed analysis of AHC revealed that, in global terms, the use of the Ps-1 and Ps-15 strains against F. culmorum may not be satisfactory, whereas against F. solani, the difference in results (indicating improved biometric parameters) was high. Clearly, AHC analysis made it possible to determine only the Ps-9 strain to be highly effective as a biological agent against both species of Fusarium.
Satisfactory results were also obtained in our own previous works [39,45,46], which used the P. luteola SP0113 strain characterized by the control of phytopathogens of the genus Fusarium and a number of traits of PGPB, particularly P-solubilization. The beneficial effect of water bacteria on agricultural plants was presented in the paper by Goswami et al. [42] concerning the Pseudomonas sp. strain OG. The plant growth-promoting potential of Pseudomonas sp. OG strain was shown on Pikovskaya's broth. A medium inoculated with Pseudomonas sp. OG had a growth-promoting effect resulting in high yield biometrics of chickpea and green gram plants.
Sludge disposal is an increasing environmental problem and few methods exist for sludge disposal and recycling. Recently, emphasis has been placed on the bioconversion of sludge into value-added products such as biopesticides, enzymes, bioplastics, and growth medium for PGPR as legume inoculants [47]. We have demonstrated the additional utility of sewage sludge, which can be a source of effective PGPB.
In previous studies [38], it was shown that bacteria isolated from the air, emitted by sewage treatment plants, can contribute to comparable protection against Fusarium spp. to that of soil bacteria. Among the several species belonging to Advenella, Enterobacter, Lactococcus, Proteus, Pseudomonas, and Staphylococcus, the P. putida showed the greatest positive effect on the reduction of F. culmorum and F. graminearum, both in plate and soil tests, and improved biometric parameters for control without the use of microorganisms. On the basis of the current results of principal component and AHC analysis, we showed there to be a weak antagonistic impact of the Ps-1 and Ps-15 strains on F. culmorum, whereas in the case of F. solani, the strains improved biometric parameters, i.e., root dry mass, ear dry mass, root weight, ear length, and root length. However, the Ps-9 strain proved to be highly effective against the two Fusarium species tested, which indicates the need for selection before introducing the strain for practical use in the method of biological promotion and protection of plants.

Conclusions
It can be concluded that plant growth-promoting potential is also expressed by non-agricultural bacterial strains, although this fact is not yet well-known to a broader audience. More study is needed in this area. Our results and the data from the available literature indicate that the tested strains may be effective in organic crops, because they show high resistance to a wide range of plant pathogens and in most cases positively affect plant growth.
Our experiment shows that the strains of bacteria used derived from activated sludge have excellent antagonistic properties against Fusarium spp. These PGPB demonstrate a wide range of biochemical activities that are useful in preventing the spread of plant pathogens. Moreover, they have an indirect impact on plant growth under more favorable environmental conditions. In summary, the strains used have the potential to be applied as a universal biological control agent, particularly the most effective strain Pseudomonas sp. Ps-9. To fully understand the mechanism of PGP action, future work should focus on genomic and proteomic analysis (next generation sequencing) of different antagonistic PGPB to detect the genes responsible for controlling phytopathogenic fungi.


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Induction of Ovulation Using Ovaprim And Its Impact on Reproductive Parameter of Clarias gariepinus

Sustainability of the aquaculture industry can be secure through the induce breeding technique. One way that induce breeding can do is to increase the rate of maturation and ovulation in making sure the availability of good quality fish seeds all year round. Here, we investigate how Ovaprim hormone impact the reproduction of Clarias gariepinus including spawning rate, fecundity, hatching and survival rate. For this purpose, six broodstocks were divided in three experimental treatments and injected intramuscularly with different doses of Ovaprim hormone of T1: 0.25ml/kg for female and 0.125ml/kg for male, T2: 0.5ml/kg for female and 0.25ml/kg for male and T3: 1.0ml/kg for female and 0.5ml/kg for male. According to our results, broodstock injected with Ovaprim hormone of dose of T3: 1.0ml/kg for female and 0.5ml/kg for male showed higher eggs production and hatching rate compared to other treatment. The results of this study suggest that a dosage of 1.0ml/kg Ovaprim for female and 0.5ml/kg for male have a better effect on the reproductive parameter of Clarias gariepinus.

INTRODUCTION
In aquaculture industry, the success of fish farming enterprises depend on the availability of good quality fish seeds as a stock for sustainable of the fish production to meet the needs (Olumuji and Mustapha, 2012). In natural environment, fish seed usually can be produce by natural reproduction, collected from wild but all this technique contributed to the low quality and quantity of seed as well as seasonal dependence. Therefore, breeding technology such as induce breeding is crucial needed to overcome these problem (Nuraini et al., 2017).
Induced breeding through hormone treatment and artificial incubation of fertilized egg has advantages of better rate of fertilization and hatching, better conditions for growth and better protection of larvae against unfavourable environmental condition and predators (Olaniyi and Akinbola, 2013). Therefore, study on the usage of hormonal dosage could help to determine the best concentration of hormone that could increase the production of larvae.
There are various hormones that can be used in induced breeding for seed production including pituitary extract or hypophysis from similar or different fish, Deoxycorticosterone Acetate (DOCA), Ovaprim, Ovulin, Ovatide, Human Chorionic Gonadotropin (HCG), Ovopel, Dagin and Aquaspawn (Brzuska and Adamek, 1999;Cheah and Lee, 2000;Zohar and Mylonas, 2001;Hassan et al., 2018). The commercially available synthetic inducing hormones in readymade containing GnRH and dopamine blocker receptor such as ovaprim, ovopel, dagin and aqua spawn are becoming very popular and found to be efficient in successful spawning of fishes (Hassan et al. 2018).
Ovaprim is one of the most popular and effective hormones to stimulate the maturation of male and female adult fish. Ovaprim is used as an effective spawning inducer for artificial breeding of fishes (Chaube et al., 2014). Ovaprim is made from Salmon Gonadotropin Releasing Hormone (SGnRGH) and Dopamine antagonist in a stable solution, prepared in glycerine and alcohol at certain quantity (Sasmita, 2016). Ovaprim are used to quicken the ovulation process in female catfish and make sperm more fertile in male fish. Ovaprim can accelerate the final oocytes maturation and induced spawning (Zadmajid et al., 2017). Fish treated with the Ovaprim exhibited increase in gonadosomatic index, egg diameter, and wet weight relative to controls. Fish that injected with high dosage of hormones produce large number of egg production (Zadmajid et al., 2017).
Artificial breeding of fish, especially C. gariepinus, remains the most capable and reliable way to make sure obtainability of good quality fish larvae (Hassan et al., 2018). Clarias gariepinus hardly reproduces in captivity (Hassan et al., 2018). In order to make C. gariepinus to reproduce faster throughout the year, induced breeding technique was apply to the male and female fish in order to produce sperms and eggs in control condition. Thus, in the present study, the following questions were addressed: (1) How is the egg production of Clarias gariepinus affected under different Ovaprim hormones concentration? (2) How does the exposure of egg to different concentration of Ovaprim hormones affect the embryonic development of Clarias gariepinus? (3) How is the hatching rate of Clarias gariepinus affected under different Ovaprim hormones concentration?

RESEARCH METHOD Broodstocks Selection
This study was conducted at Aquaculture Laboratory, Blok Pakar, Campus Sultan Abdul Jalil, University Pendidikan Sultan Idris (UPSI), Tanjung Malim Perak. Selected broodstocks of Clarias gariepinus (3 males and 3 females) were acquired from commercial farmer in Hulu Bernam, Selangor. The weight of broodstocks ranged from 2.3 kg-1.025 kg for male and 2.76 kg-1.6 kg for female. Males and females of C. gariepinus can be determine by looking at the genital papillae of the fish. Male fish have genital papillae that is coned shape and female fish have genital pore that is rounded shape. They were transported back to the Aquaculture Laboratory in UPSI. All of the broodstocks were separated in different aquarium tank. The fish were fed with commercial pellet food.

Hormone Preparation and administration
Ovaprim hormone were purchased from Syndel Laboratories Ltd, USA. For this experiment, ovaprim hormone was prepared for three different doses (Table 1) of 0.125mL/kg (BW), 0.25mL/kg (BW) and 0.5mL/kg (BW) for male and 0.25mL/kg (BW), 0.5mL/kg (BW) and 1.0mL/kg (BW) for female. The females were given full dosage of hormone while the males were received half of the doses administered to the females (Viveen et al., 1985). Fish were injected intramuscularly above the lateral line using 1mL syringe and 5mL syringe

Experimental Design
Immediately after administering the hormone, males and females broodstock were released into separate aquarium tank. Approximately, after 10-11 hours, the males and females broodstock reached final oocytes maturity stage by checking the female by pressing the abdomen. The eggs from female's fish were striped out by pressing the abdomen and the eggs was put in the bowl and weight. Males fish was sacrifice to collect sperm. Then, sperm from male was cut into pieces and mixed with the egg by gently stir with chicken feather in order to fertilized the eggs. Along this, latency period and eggs production were also measured. Eggs production was determined by taking sample of eggs (0.25g) from the total eggs released by female. Total number of eggs in 1g were counted and multiplied by total weight of eggs. Fertilized eggs (10g) were transferred into different 2L aquaria (T1, T2 and T3) for incubation. The eggs were monitored until hatching (20 hours) and the hatching rate were determined by calculate the total number of hatched eggs and divided by the number of eggs.

Reproductive parameter
The reproductive parameter measure in this study were egg weight g.bw hatching percent (%) and larval survival (%). These parameters were measured as follow: Hatching percent = number of viable embryos / total number of eggs x 100 Egg weight/g. bw = the number of collected eggs (g) / total body weight (g)

Statistical Analysis
Statistical software package SPSS version 24.0 was used for data analysis. Data normality was investigated by Kolmogrov-Smirnov test. Differences between mean were analyzed by One-way analysis of variance (ANOVA) followed by Tukey comparison P< 0.05.

RESULTS AND DISCUSSION Embryonic Development
Maturation, ovulation and spawning were achieved from females injected with Ovaprim hormone at 0.125ml/kg (T1), 0.50ml/kg (T2) and 1.0ml/kg (T3). Injected females spawned at 10-11 h after injection. The embryonic development characteristic stages are described in Table 2.  Break-off of the embryo out of the egg capsule through the tail.

Hatching Rate
There is no significant effect of hormone dosage on the hatching rate of C. gariepinus eggs (Table 4; F2,5 = 9.616, P = 0.050). A pairwise comparison (Turkey) was then used to determine the hatching rate between groups. There are significant differences (F2,5= -705.500, P = 0.045) of hatching rate between lower dose of Ovaprim hormone (T1) with the high dose of hormone (T3). Meanwhile, the percentage of egg hatched into larvae in T2 (standard dose) was 15.21% respectively (Figure 1).  1. Hatching rate of Clarias gariepinus egg from a female injected with Ovaprim hormone at 0.25ml/kg (T1), 0.50ml/kg (T2) and 1.0ml/kg (T3). Data are means ± standard error.* significant difference at 5% level.

CONCLUSION
The effect of Ovaprim hormone on eggs production, embryonic development and hatching rate on Clarias gariepinus was studied. In this study, the concentration of Ovaprim hormone injected on female broodstock (T1= 0.25 ml/kg, T2= 0.5 ml/kg , T3= 1.0 ml/kg) has effect on the eggs production of C. gariepinus. The result show high concentration 1.0ml/kg (T3) of Ovaprim hormone injected into female broodstock resulted in more eggs production compare to the low concentration. Different concentration of Ovaprim hormone used in this study also affect the embryonic development of C. gariepinus. In this experiment, the lower concentration (T1) of Ovaprim hormone affect the embryonic development of C. gariepinus by trigger the eggs to hatched faster compare to control (T2) and high concentration (T3). The effect of Ovaprim hormone on the hatching rate was also studied. In this study, the concentration of Ovaprim hormone effect the hatching rate of C. gariepinus. The result shows that high concentration (T3) of hormone increased the hatching rate compare to low concentration (T1) and control (T2).
In this study, the concentration of ovaprim hormone play an important role for eggs production in C. gariepinus. Higher hormone concentration, 1.0ml/kg (T3), used to injected female fish has produce higher number of eggs compare to other treatment (T1 and T2). This result are similar with Hassan et al., (2018), Nuraini et al., (2017 and Sharma et al., (2010) where high dosage of hormone influenced the weight of eggs produced as the increase in dosage resulted in more eggs being produced by the fish.
The concentration of Ovaprim hormone also has affected the hatching rate of C. gariepinus eggs. High concentration hormone, T3 (male=0.5ml/kg, female=1.0ml/kg) have contribute to produced more hatching rate of C. gariepinus compare to low concentration, T1 (male=0.125ml/kg, female=0.25ml/kg). This result is similar to Nuraini et al., (2017) where their study showed that Cyclocheilichthys apogon,breeding using high concentration of Ovaprim hormone produced greater hatching rate. Ghosh et al., (2012) also reported that high concentration of Ovaprim hormone produced high hatching rate of induced breeding, embryonic and larvae development of koi carp (Cyprinus carpio).

Induction Of Ovulation Using
Paul. et. al.

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In low doses, a reproductive hormone does not support the maturation of eggs for example germinal vesicle migration and breakdown and subsequently ovulation (Rottmann et al., 1991). At high concentration doses, the combination of sGnRH and domperidone in Ovaprim solution might stimulated gonadotropin secration in the pituitary gland of the fish and therefore, induced the ovulation and maturation of gonad (Nuraini et al., (2017) which explain the higher egg production and hatching rate at increased dosage of ovaprim. In addition, the optimum dosage of stimulating hormone results in better ovulation and improves egg quality. Prostaglandin plays an important role in inducing ovulation and significantly influences fertilization and hatching rates. This is because prostaglandin contains arachidonic acid derived from essential fatty acids that determine the egg quality (Rottman et al., 1991). In addition, the quality of eggs is influenced by several factors including the hormone levels, nutrition genetics and the environment (Rottman et al., 1991). The quality of eggs is reflected in higher fertilization and hatching rates. In this study the effect of nutrition, genetic and environmental factor on the reproduction of catfish was not investigate. Therefore, future work is needed to look at this factor as it can influence the ovulation and also the quality of egg.
The use of Ovaprim in induced breeding appear to be a promising alternative to seed production. However, results of the study showed that the Ovaprim hormone did not significantly affected the hatching rate of C. gariepinus. On the other hand, Ovaprim hormone significantly affected the egg production. In conclusion, the results in the present study showed that the Ovaprim at dose 1.0 ml/kg improves more reproductive parameter of catfish.