Evaluation of NDVI, SPAD values and yield of two different maize (Zea mays l.) genotypes under foliar fertilisation

Ensuring global food security has become a matter of great concern with the constantly increasing population growth, resulting in rising food demands. Simultaneously, climate change and land degradation pose major risks to agricultural production. Maize is one of the most produced crops globally and maize yields must be increased to meet the population’s needs. Fertilisation is considered indispensable for the crop growth and development. Foliar fertilisation, unlike root fertilisation, enables rapid access of nutrients to plants while sustaining the environment. Our research was carried out at Látókép in 2021, where, foliar sprays of Natur Plasma T biostimulant, Natur Active complex foliar fertiliser, Zinc and Sulphur Mono additives were applied at the 8-leaf stage on two maize hybrids, Mv 352 (FAO 350) and Mv Anissa (FAO 510). The main objectives were to examine the treatment’s effect on crops at critical phenophases (12-leaf stage, silking, maturity), besides determining its impacts on the harvested yields. Based on our findings, foliar nutrients had positively influenced the NDVI and SPAD values of both crops. Furthermore, in comparison with the control plots, the yield of Mv Anissa was 9% higher, while that of Mv 352 was 5.4% higher. Consequently, Mv Anissa produced the highest yield of 21.345 t/ha, i.e. 2.8 tons higher than that obtained by Mv 352. Moreover, the treatment increased their thousand-grain weight. Thereby, our study demonstrates the efficiency of the foliar fertilisation method in improving maize vegetative growth and development in addition to its productivity by enhancing its final yield

The energy balance of maize production – alternative approaches

Agricultural production is a crucial area, perhaps the most important for humanity. This is the only area which cannot be avoided. Therefore, it is of utmost importance to know how sustainable the system is in the long run as regards energy consumption. We have chosen the maize production sector as the main focus of this study. This crop is especially important all over the world, therefore; it requires significant input also in terms of energy. Currently, the system of maize production (as with the others) operates as an open energy system. This study aims to examine how much of the agricultural land’s energy demand could be met with the help of the byproducts of 1 hectare of agricultural land - operating as a closed system, using only the remaining maize stalk and cob byproducts for energy - under the conditions of Hungarian maize production. Energy demand is largely determined by the land’s fertilizer requirement, followed by the input factor of the energy demand of the machinery during earthwork and transport. The study assumes that the energy from the byproducts of maize production will be used exclusively with biogas technology. This can even be implemented on a county level. The final question is whether the maize production system will be able to sustain itself solely by using its own byproducts

Tenyészedényes szilvafajták- és alanykombinációik fenofázisai (2011-2013)

18 szilva alany-nemes kombinációt vizsgálunk Kecskeméten a Kertészeti Főiskola bemutató kertjében. Kétféle öntözéssel állítottuk be a kísérletet. Egy-egy oltvány kombináció az öntözés függvényében 6 ismétlésben szerepelt. A vegetációs időszak 2011-ben március 15-18.; 2012-ben március 16-19. és 2013-ban március 12-április 2. rügypattanással indult. Azt követte rövidebb -hosszabb időszak után a zöldbimbós állapot, majd néhány napon belül a fehérbimbós állapotot és a virágzás kezdetét regisztráltuk. A fővirágzás 7-10 napig tartott, kivéve 2012-ben, amikor az április 8-i reggelre a fagy (-7C) elpusztította a virágokat. A virágzást követően 3 hullást tapasztaltunk ezek nem köthetők naptári időponthoz: a virágzás utáni hullás, a júniusi és a szüret előtti hullás. 2013-ban az egyes a hullások nem voltak számottevőek. Legkorábban a Katinka/ St Julien A-ról 2011-ben még nem volt számottevő termés, 2012-ben július 17-én, és 2013-ban július 9-én szedtük le a szilvát. A Cadanska lepotica július 21-30 ött ért meg, ezeket követte a Topfive július 19-augusztus 6., a Toptaste augusztus 5- 23., a Jojo augusztus 2-26., és az érésidőt a Topper és a Katinka/Mirobalan kombináció fejezte be augusztus 22-szeptember eleje közötti szedéssel. A szüretet követően nem sokkal (7-10 nappal) a lombszíneződés és lombhullás is folyamatosan elkezdődött, de a lomhullás vége csak az első nagyobb fagyok alkalmával, október 24-november 26 között figyeltük meg

Application of spatio-temporal data in site-specific maize yield prediction with machine learning methods

In order to meet the requirements of sustainability and to determine yield drivers and limiting factors, it is now more likely that traditional yield modelling will be carried out using artificial intelligence (AI). The aim of this study was to predict maize yields using AI that uses spatio-temporal training data. The paper has advanced a new method of maize yield prediction, which is based on spatio-temporal data mining. To find the best solution, various models were used: counter-propagation artificial neural networks (CP-ANNs), XY-fused Querynetworks (XY-Fs), supervised Kohonen networks (SKNs), neural networks with Rectangular Linear Activations (ReLU), extreme gradient boosting (XGBoost), support-vector machine (SVM), and different subsets of the independent variables in five vegetation periods. Input variables for modelling included: soil parameters (pH, P2O5, K2O, Zn, clay content, ECa, draught force, Cone index), micro-relief averages, and meteorological parameters for the 63 treatment units in a 15.3 ha research field. The best performing method (XGBoost) reached 92.1% and 95.3% accuracy on the training and the test sets. Additionally, a novel method was introduced to treat individual units in a lattice system. The lattice-based smoothing performed an additional increase in Area under the curve (AUC) to 97.5% over the individual predictions of the XGBoost model. The models were developed using 48 different subsets of variables to determine which variables consistently contributed to prediction accuracy. By comparing the resulting models, it was shown that the best regression model was Extreme Gradient Boosting Trees, with 92.1% accuracy (on the training set). In addition, the method calculates the influence of the spatial distribution of site-specific soil fertility on maize grain yields. This paper provides a new method of spatio-temporal data analyses, taking the most important influencing factors on maize yields into account

Spatial Variability of Soil Properties and Its Effect on Maize Yields within Field—A Case Study in Hungary

To better understand the potential of soils, understanding how soil properties vary over time and in-field is essential to optimize the cultivation and site-specific technologies in crop production. This article aimed at determining the within-field mapping of soil chemical and physical properties, vegetation index, and yield of maize in 2002, 2006, 2010, 2013, and 2017, respectively. The objectives of this five-year field study were: (i) to assess the spatial and temporal variability of attributes related to the maize yield; and (ii) to analyse the temporal stability of management zones. The experiment was carried out in a 15.3 ha research field in Hungary. The soil measurements included sand, silt, clay content (%), pH, phosphorous (P2O5), potassium (K2O), and zinc (Zn) in the topsoil (30 cm). The apparent soil electrical conductivity was measured in two layers (0–30 cm and 30–90 cm, mS/m) in 2010, in 2013, and in 2017. The soil properties and maize yields were evaluated in 62 management zones, covering the whole research area. The properties were characterized as the spatial-temporal variability of these parameters and crop yields. Classic statistics and geostatistics were used to analyze the results. The maize yields were significantly positively correlated (r = 0.62–0.73) with the apparent electrical conductivity (Veris_N3, Veris_N4) in 2013 and 2017, and with clay content (r = 0.56–0.81) in 2002, 2013, and 2017