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Stand evaluation, crop estimation and yield analysis of winter wheat for the optimization of yields
103-109Views:125The authors have been carrying out stand evaluation, crop estimation and yield analysis in winter wheat since 2012. The sampling areas were assigned at the fields of the Training Farm of the Faculty of Agricultural and Food Sciences of Széchenyi István University Mosonmagyaróvár according to the structure of the cropping system. According to their observations the value of field emergence is always lower than the laboratory germination. The weak emergence is important because the lower plant density cannot be compensated by the increased tillering in spite of having larger plant growth space. It is proven by the fact that they detected strong productive tillering even at 5 and 10 mm plant spacing while there were single-spiked plants at 40-50 mm plant spacing as well. The analysis revealed that the total ear mass and grain mass of wheat plants bearing two or more ears is almost the double than that of the single-spiked plants. It was a further basic experience that the largest ear of ”multiple-spiked” plants is always heavier than the single ear of one-spiked plants. Plants with intense tillering and more ears demonstrate the importance of proper seedbed preparation and drilling and the significance of sowing good quality seeds. These are the factors that determine field germination and emergence, influence the speed and intensity of initial development and by all these factors the sufficient productive tillering. The authors emphasize the use of exact and objective methods at crop estimation, e.g. the relationship between the ear mass and the yield which is in strong correlation whilst ear length and grain mass are not suitable for a precise estimation. The authors conclude that crop estimation and yield analysis must be inevitable tools of modern crop production and will be particularly important in precision agriculture. These tools also qualify the job done by farmers and helps to identify the areas that require special attention.
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Investigation of combining ability and superiority percentages for yield and some related traits in yellow maize using line × tester analysis
5-14Views:233Combining ability estimation is an important genetic attribute for maize breeders in anticipating improvement in productivity via hybridization and selection. This research was carried out to investigate the genetic structure of the 27 F1 maize hybrids established from nine lines derived from Maize Research Department and three testers, to determine general combining ability (GCA), determine crosses showing specific combining ability (SCA) and superiority percentages for crosses. Nine lines, three testers, 27 F1 hybrids and two check commercial hybrids (SC162 and SC168) were studied in randomized complete block Design (RCBD) with three replications during 2016. The results of mean squares showed that significant and highly significant for most studied traits (days to 50% tasseling, days to 50% silking, plant and ear height, ear position, ear length, no. of kernels per row, 100-kernel weight and Grain yield). Estimates of variance due to GCA and SCA and their ratio revealed predominantly non-additive gene effects for all studied traits. Lines with the best GCA effects were: P2 (line 11) and P6 (line 21) for grain yield, for testers Gm174 and Gm1021 had significant GCA effects for grain yield. The hybrids P5×Gm1021, P6×Gm1021, P7×Gm1021, P8×Gm1002, P9×Gm1002 had significant and negative SCA effects for grain yield. Crosses P1×Gm174, P2×Gm1002, P5×Gm1021, P6×Gm174, P6×Gm1021, P7×Gm1021, P8×Gm1002, P9×Gm1021 were the best combinations manifested and significant superiority percentages over than check varieties (SC162 and SC168) for most studied traits. Therefore, these hybrids may be preferred for hybrid crop development.
Abbreviations: GCA general combining ability; SCA specific combining ability
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Biomass production estimation of processing tomato using AquaCrop under different irrigation treatments
131-136Views:189The wiser usage of irrigation water is inevitable in the future. Irrigation has very high input cost; therefore, farmers must carry out irrigation with care. Also, the effect of irrigation on crops has a big role in decision making. Modeling provides a possibility to evaluate this effect. AquaCrop, as a crop production simulation model has great potential in this field. The accuracy of tomato biomass yield prediction of the model was tested in this research. For collecting the necessary data, a field experiment was conducted at Szarvas on processing tomato with different water supplies, such as 100% (I100), 75% (I75), 50% (I50) of potential evapotranspiration and a control with basic water supply (C). The relation of the simulation and actual biomass yields was evaluated during the season. Very good correlation was found between the modelled and the actually harvested data. The data for the control and I100 treatments showed higher correlation than the I75 and I50. The relationship for all of the data was moderately strong. Miscalculations occur mostly when the dry biomass yield reaches
7 t ha-1. The accuracy of the model was evaluated with the use of mean absolute error (MAE) and root mean squared error (RMSE) values. The least error was found in the C treatment, which means 0.34 MAE and 0.45 t ha-1 RMSE. The simulation resulted in higher errors in the I75 and I50 treatments. -
The Effect of Atmosperical Aridity on the Changes of Quantitative Parameters of Horticultural and Arable Crops
40-45Views:103The occurence of atmospherical drought causes serious water-supply problems in the most cases of our domestic agricultural plant species. This paper was studied, how can we quantificate the atmospherical drought, with the help of a low input (relative humidityof the air, temperature) index. If this index (LSZI) characterized the atmospherical drought well, it will suitable to estimate the yield amount of agricultural plants.
The index elaborated by the authors was tested on county average crop yield of 14 agricultural plant species. Moreover we compared the atmospherical drought index (LSZI) to other aridity parameters, how suitable for estimate the yield amount.
Result of experiments show that, the atmospherical drought index (LSZI) can be used well by several agricultural plant species in especially coern and sugar-beet to estimate yield amount. Excellent results were found by comparison to other aridity indexes, this means it is worth using in the aridity researches in the future.