Seasonal predictability of weather and crop yield in regions of Central European continental climate

Juhász, Csaba; Gálya, Bernadett; Kovács, Elza; Nagy, Attila; Tamás, János; Huzsvai, László

doi:10.1016/j.compag.2020.105400

Cited by 15 publications

(11 citation statements)

References 24 publications

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“…In the period from March to June, total rainfall was 92.8 mm less than the 30-year average. According to climate change model predictions, it is expected that Hungary will experience more severe drought events, while on the other hand, more extreme precipitation events in the future are also foreseen (Juhász et al [ 34 ]).…”

Section: Resultsmentioning

confidence: 99%

Mitigating the Negative Effect of Drought Stress in Oat (Avena sativa L.) with Silicon and Sulphur Foliar Fertilization

Kutasy

Buday-Bódi

Virág

et al. 2021

Plants

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A field experiment was carried out in the 2020–2021 growing season, aiming at investigating the abiotic stress tolerance of oat (Avena sativa L.) with silicon and sulphur foliar fertilization treatments and monitoring the effect of treatments on the physiology, production and stress tolerance of winter oat varieties. In the Hungarian national list of varieties, six winter oat varieties were registered in 2020, and all of the registered varieties were sown in a small plot field experiment in Debrecen, Hungary. The drought tolerance of the oat could be tested, because June was very dry in 2021; the rainfall that month totaled 6 mm only despite a 30-year average of 66.5 mm, and the average temperature for the month was 3.2 °C higher than the 30-year average. Foliar application of silicon and sulphur fertilizers caused differences in the photosynthesis rate, total conductance to CO2, transpiration, water use efficiency, leaf area, chlorophyll content, carotenoid content, thousand kernel weight (TKW) and yield of winter oat. The application of silicon significantly increased the photosynthesis rate (16.8–149.3%), transpiration (5.4–5.6%), air–leaf temperature difference (16.2–43.2%), chlorophyll (1.0%) and carotenoid (2.5%) content. The yield increased by 10.2% (Si) and 8.0% (Si plus S), and the TKW by 3.3% (Si) and 5.0% (Si plus S), compared to the control plots. The plants in the control plots assimilated less CO2 while transpiring 1 m3 water more than in the Si, S or Si plus S fertilized plots. The effect of the silicon varied from 9.0 to 195.4% in water use efficiency (WUE) in the three development stages (BBCH52, BBCH65 and BBCH77). A lower leaf area index was measured in the foliar fertilized plots; even so, the yield was higher, compared to that from the control plots. Great variation was found in response to the foliar Si and S fertilization among winter oat varieties—in WUE, 2.0–43.1%; in total conductance to CO2, 4.9–37.3%; in leaf area, 1.6–34.1%. Despite the droughty weather of June, the winter oat varieties produced a high yield. The highest yield was in ‘GK Arany’ (7015.7 kg ha−1), which was 23.8% more than the lowest yield (‘Mv Kincsem’, 5665.6 kg ha −1). In the average of the treatments, the TKW increased from 23.9 to 33.9 g (41.8%). ‘Mv Hópehely’ had the highest TKW. Our results provide information about the abiotic stress tolerance of winter oat, which, besides being a good model plant because of its drought resistance, is an important human food and animal feed.

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Section: Resultsmentioning

confidence: 99%

Mitigating the Negative Effect of Drought Stress in Oat (Avena sativa L.) with Silicon and Sulphur Foliar Fertilization

Kutasy

Buday-Bódi

Virág

et al. 2021

Plants

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“…This provides an unpredictable water supply for the vegetation and makes crop and fruit production vulnerable. An increasing number of heat days during the ripening of cereals and the lack of precipitation can lead to a significant loss of yield and to a deterioration in quality [26]. It is expected that the Tisza river basin will experience more severe drought events according to the climate change models' predictions, while on the other hand, more extreme precipitation events in the future are foreseen.…”

Section: Study Sitementioning

confidence: 99%

Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics

et al. 2021

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Due to the increasing global demand of food grain, early and reliable information on crop production is important in decision making in agricultural production. Remote sensing (RS)-based forecast models developed from vegetation indices have the potential to give quantitative and timely information on crops for larger regions or even at farm scale. Different vegetation indices are being used for this purpose, however, their efficiency in estimating crop yield certainly needs to be tested. In this study, wheat yield was derived by linear regressing reported yield values against a time series of six different peak-seasons (2013–2018) using the Landsat 8-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). NDVI- and SAVI-based forecasting models were validated based on 2018–2019 datasets and compared to evaluate the most appropriate index that performs better in forecasting wheat production in the Tisza river basin. Nash-Sutcliffe efficiency index was positive with E1 = 0.716 for the model from NDVI and for SAVI E1 = 0.909, which means that the forecasting method developed and performed good forecast efficiency. The best time for wheat yield prediction with Landsat 8-SAVI and NDVI was found to be the beginning of full biomass period from the 138th to 167th day of the year (18 May to 16 June; BBCH scale: 41–71) with high regression coefficients between the vegetation indices and the wheat yield. The RMSE of the NDVI-based prediction model was 0.357 t/ha (NRMSE: 7.33%). The RMSE of the SAVI-based prediction model was 0.191 t/ha (NRMSE 3.86%). The validation of the results revealed that the SAVI-based model provided more accurate forecasts compared to NDVI. Overall, probable yield amount is possible to predict far before harvest (six weeks earlier) based on Landsat 8 NDVI and SAVI and generating simple thresholds for yield forecasting, and a potential loss of wheat yield can be mapped.

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“…On the one hand, algorithms for soil, crop and weather parameters are combined, where the first two are deterministic, while the third is a stochastic element. Weather is assumed to behave according to a chaotic model and shows scale-independent distribution, considerably increasing the uncertainty in the predictions; and even if a relationship is found between weather variability and crop yield variation as a response, crop yield cannot be estimated since weather cannot be predicted in advance [13]. For example, Roudier et al [14] predicted for West Africa that rainfall changes, still uncertain in climate projections, have the potential to either exacerbate or mitigate the impact of climate change, depending on whether rainfall decreases or increases.…”

Section: Introductionmentioning

confidence: 99%

“…Figure13. Heat stress units in the four investigated regions divided into two periods(1970-1990 and 1991-2019).…”

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confidence: 99%

Can Technological Development Compensate for the Unfavorable Impacts of Climate Change? Conclusions from 50 Years of Maize (Zea mays L.) Production in Hungary

et al. 2020

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The goals of our study were to evaluate the historical aspects of maize (Zea mays L.) production in Hungary, and to provide a prognosis for the yield for 2050 based on the trends of temperature, precipitation, and climatic water balance changes. Different climate zones for the period of 1970–2019 were investigated by means of correlation analyses, normality tests, time series analysis, and multiple linear regression analysis. Two well-distinguishable linear trends in the yields were found, the first representing large-scale farming, and the second starting with the change of the socio-economic system in 1989. The annual amount of precipitation showed high variations both spatially and temporally, although no significant change was identified for the last five decades. In the period 1990–2019, not only were higher temperatures characteristic, but the frequency of extreme high temperature values (Tmax > 30 °C) also increased. We quantified the heat stress, expressing it in heat stress units (HSU, °C) derived from the heat-sum of the daily maximum air temperature values above 30 °C. By 2050, the average increase in HSUs may reach 35 °C. Increasing HSU causes yield depression; according to our estimations, a 1 °C increase in HSU results in a 23 kg ha−1 yield depression of maize. Taking the unfavorable effect of heat stress and technological development into consideration, the average domestic yield of maize will be 8.2 t ha−1. Our study revealed that without taking technological development into consideration, prediction models may overestimate the adverse effect of climate change on crop production.

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Seasonal predictability of weather and crop yield in regions of Central European continental climate

Cited by 15 publications

References 24 publications

Mitigating the Negative Effect of Drought Stress in Oat (Avena sativa L.) with Silicon and Sulphur Foliar Fertilization

Mitigating the Negative Effect of Drought Stress in Oat (Avena sativa L.) with Silicon and Sulphur Foliar Fertilization

Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics

Can Technological Development Compensate for the Unfavorable Impacts of Climate Change? Conclusions from 50 Years of Maize (Zea mays L.) Production in Hungary

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