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In a long-term experiment set up in Martonvásár (N 47°21′, E 18°49′), Hungary in 1960 on a humous loam soil of the chernozem type, the effect of five crop production factors in increasing maize yields was studied in seven treatments. The factors studied were soil cultivation, fertilisation, plant density, variety and weed control. All the factors had a favourable and an unfavourable level. Yield data recorded over 42 years were evaluated using analysis of variance and stability analysis. The highest yield (8.59 t ha ⁻¹ ) was obtained when all the production factors were favourable and lowest (2.09 t ha ⁻¹ ) when these factors were unfavourable. When only one factor was unfavourable and all the other factors were favourable the following yields were obtained (t ha ⁻¹ ): soil tillage: 8.32, fertilisation: 5.21, genotype: 4.98, plant density: 6.31 weed control: 7.01. The crop production factors contributed to the increase in maize yield in the following ratios (%): fertilisation 30.6, variety 32.6, plant density 20.2, weed control 14.2, soil cultivation 2.4. The highest value of the coefficient of variation (CV%) was obtained when all the production factors were at the unfavourable level (45.7%) and when weed control or fertilisation were unfavourable (36.6% and 34.8%, respectively), while the lowest value was recorded when all the factors were favourable (19.5%). The significant treatment × year interaction could be attributed principally to treatments in which weed control, fertilisation, genotype or all the factors were unfavourable. The regression coefficient of linear regression analysis provided a satisfactory characterisation of the stability of the treatments in different environments, while the distance between the straight lines expressed the yield differences between the treatment pairs. The AMMI (Additive Main Effect and Multiplicative Interaction) model proved to be a valuable approach for understanding agronomic treatment × environment interactions and assessing the mean performance and yield stability of treatments.

In maize, plant density has a considerable influence on the rate of dry matter accumulation and on its partitioning between vegetative and reproductive sinks. The aim of the present research was to use the first, second and third derivatives of the Richards function (RF) for growth analysis on maize hybrids grown at various densities. In two-factorial split-plot experiments carried out in Martonvásár, Hungary in 1997–1999 the growth analysis method was used to examine the effect of six plant densities (20, 40, 60, 80, 100 and 120 thousand plants ha ⁻¹ ) on the growth of three maize hybrids (Mara, Mv 355, Florencia) with different vegetation periods. Plant density had a significant effect on the dynamics of dry matter accumulation, absolute growth rate (AGR) and absolute acceleration rate (AAR). There was a significant reduction in the asymptotic maximum (A) and growth parameters (AGR, AAR) of the whole plant and of the individual plant organs (stalk, leaf, ear and grain yield), while the parameters of the leaf area index (LAI) increased significantly with a rise in the plant density. The usefulness of the RF for approximating the growth processes of maize plants and individual plant parts was confirmed statistically.

The effect of sowing date, N fertilisation and genotype on the grain yield and yield stability of maize was studied between 1991 and 2006 in a long-term N fertilisation experiment set up on chernozem soil in Martonvásár, Hungary. The N treatments (0, 60, 120, 180 and 240 kg ha ⁻¹ ) represented the main plot of the three-factor, split-split-plot experiment, with the sowing date (early, optimum, late, very late) in the sub-plots and hybrids from different maturity groups in the sub-sub-plots. The highest yields were obtained for the early and optimum sowing dates (8.712 and 8.706 t ha ⁻¹ ). Compared with the optimum sowing date, a delay of ten or twenty days led to yield losses of 5% and 12.5%, respectively. In the late and very late sowings and in years with unfavourable weather conditions, yield increments were only observed up to an N rate of 60 kg ha ⁻¹ , while in the early and optimum sowings and in favourable years yield increments were significant up to 120 kg ha ⁻¹ N. Yield stability was smallest in the early and very late sowings, in the control and for high N rates, and in the early and late maturity hybrids. It can be concluded that high yields and yield stability are not mutually exclusive.

The effect of sowing date, N fertiliser rate, plant density and genotype on the yield stability of maize was analysed using 15-year data from a 5×4×5-factorial sowing date experiment, 35-year data from a two-factorial N fertilisation experiment and 25-year data from a two-factorial plant density experiment. Stability analysis on the experimental treatments was carried out using the variance and regression methods. Among the variance parameters, the ecovalence (W), the stability variance (σ²) and the yield stability (YS) were calculated. Based on the data of the sowing date experiment the optimum sowing date (Apr. 24) or sowing ten days later (May 5) were found to be the most stable due to the low, non-significant values of the variance parameters and the values close to unity for the regression coefficients (b). Although early sowing (Apr. 14) led to a significantly higher yield than late sowing, the yield stability was poorer for early sowing. In the long-term N fertilisation experiment the variance parameters indicated the least yield fluctuation at N rates of 80 and 160 kg ha^-1, though the yield stability (YS) parameter for the 240 kg ha^-1 N rate was also above-average. Regression analysis showed that the yield level and yield stability were the same in all environments for the 160 and 240 kg ha^-1 N rates. The stability of the 80 kg ha^-1 N rate was similar, but the yield level was approx. 1.3 t ha^-1 lower. The yield stability of the plant density response of the maize hybrids was different in each maturity group (FAO number). The stable plant density range was broadest (50-90 thousand plants ha^-1) in the FAO 200-299 group. As the vegetation period lengthened the stable plant density range narrowed and shifted towards lower plant densities (for the FAO 400-499 and FAO 500-599 maturity groups: 50-70 thousand plants ha^-1). The variance and regression parameters of stability analysis both contributed to the characterisation of the stability of the genotypes and cropping systems investigated. It can be concluded from the results that high yields and yield stability are not necessarily mutually exclusive.

The effect of various fertiliser treatments on the yield of maize hybrids was studied on the basis of 26 years of data obtained in a long-term bifactorial split-plot experiment set up in 1967. The seven treatments (NPK ratio 2:1:1) applied were as follows (rates per hectare): 1. Control (no fertiliser), 2. 100 kg NPK, 3. 200 kg NPK, 4. 300 kg NPK, 5. 400 kg NPK, 6. 600 kg NPK, 7. 800 kg NPK. The maize was grown with the conventional cultivation techniques in continuous cropping. The results of analyses carried out with three different methods (analysis of variance, cumulative yield analysis and regression analysis) all indicated that under the given conditions the yield of maize hybrids was highest at an NPK fertiliser rate of 200-400 kg ha ^-1 . The effect of fertilisation on the maize yield was significant in 21 of the 26 years. Combined analysis of variance for the years showed that the year effect (quantity of rainfall) had the greatest effect on the maize yield, but although the year effect had a fundamental effect on the yield level it did not influence the fertiliser response pattern. The fertiliser responses of the maize hybrids were described by fitting four types of functions (quadratic, square root, inverse exponential, linear-plateau) to the yield data. It was found that when selecting the best function a consideration of the regression deviations (measured yield - calculated yield) was just as important as the coefficient of determination (R ² ). In 12 of the 26 years the fitting of the quadratic function was not significant and overestimated the fertilisation optimum. The fertiliser response curve generally has a broad maximum which is far better described by the square root function than by the quadratic. If the fertiliser response pattern includes a depressive phase, a square root function should definitely be used in place of the quadratic function. If the maximum of the response surface forms a plateau (as opposed to a maximum point) a linear-plateau function or an inverse exponential function can be recommended. In the present work the linear-plateau function gave the best results.