Field experiments are commonly used to test hypotheses in agronomy, physiology, and breeding. During evolution, plants develop strategies to ensure their survival and reproduction, including under suboptimal conditions. Abiotic stress, such as extreme temperature and/or limited precipitation, is increasingly challenging high-yielding cultivars grown on different soils.
Plant growth depends on surrounding climate conditions, and every species has different optimal temperatures for growth, development, and reproduction [1
]. Relevant factors influencing plant growth and crop yield in terms of climatic conditions are indicated in Table 1
. Temperature appears to be the most relevant factor affecting growth and yield of crops. Since the late 2000s, however, this connection has come to the fore of the discussion about climate change. Many studies have described how increasing temperatures and CO2
concentrations affect plant growth (e.g., [4
The optimal temperature range for winter wheat cultivation is between 20 and 30 °C, and tolerated temperatures under which wheat is grown range between −40 and 40 °C [3
], where planting season occurs in the fall and germination starts before winter. Snow covers are endured. Quick growth begins before the summer heat [1
]. In Germany, winter wheat is sowed between mid-September and the beginning of November, and harvested late July to early August.
The influence of changes in climate parameters has already been investigated in various studies. In 2016, [5
] investigated the regional yield changes in Europe from 1901 to 2012 and the relationship between climate and yield. The minimum, maximum, and average air temperatures, as well as precipitation and drought were analyzed using the Palmer drought severity index. According to this study, the annual mean temperature increased in all regions studied. Therefore, the temperature explained most of the yield fluctuations in this study. In 2010, [6
] investigated the change in the minimum and maximum temperature, global radiation, evapotranspiration, and precipitation in relation to the change in wheat yields in the period 1975 to 2008. Based on monthly averaged climate data for the growing periods of sowing, emergence, flowering
, and ripening, the authors found no common trend for effects on yields. However, the authors described that increasing temperatures harmfully affect pre-heading (yield determination) and post-heading (achievement of yield potential) periods for spring and winter barley, as well as during pre-heading of spring wheat and post-heading of winter wheat.
Some studies only considered to individual countries, for example Denmark, where the effect of day temperature, global radiation, and precipitation on wheat over a period of 17 years was investigated. Higher temperatures led to faster plant development. This resulted in a shorter stocking phase and a shorter ear formation, which led to a decrease in yield [7
Climate change also leads to increased occurrence of extreme events, such as drought or heavy precipitation [8
]. Wheat reacts differently to these changes during different stages of development. Even at germination, waterlogging can have negative consequences for plant development. High precipitation can reduce the oxygen content and increase the CO2
concentration in the soil. After a certain time, oxygen is no longer present. This influences the seed during germination and root formation. Waterlogging during the tillering phase damages the roots. This means that nutrients can no longer be absorbed, or are absorbed, but to a lesser degree. Another problem is that the redox potential decreases with decreasing oxygen content. Nitrate, sulfate, manganese, and iron oxides serve as electron acceptors, which can lead to a changed availability of the nutrients.
The strength of wintering is influenced by the degree of winter hardiness and the degree of damage. The temperature, light intensity, and adaptation of the plants to the temperatures influence the winter hardiness. Low temperatures, the duration of the frost period, snow cover, which may serve as an insulating layer, the stage of development, and the genetics of the variety influence the degree of damage [10
Wheat becomes sensitive to frost when the vegetation point of the plants is no longer protected by the leaf sheath close to the soil surface. A damaged vegetation point stops growth. This loss can be partially compensated for by later shoot development [11
]. At the time of flowering, the wheat plants are most sensitive to frost, since the flowers are directly exposed to frost, which can lead to a sterility of the flowers and thus to a reduction in the number of grains [11
]. Heat and drought at the time of flowering and grain filling affect the duration of these development phases. The grain filling phase is shortened, which means that not all assimilates can be incorporated into the grain. Heat also accelerates the senescence of the leaves. This degrades the chlorophyll in the leaves and stops the transport of assimilates into the grain. The yield is thus reduced due to the smaller grains [12
]. High precipitation at the time of grain filling also shortens this phase, so the yield decreases here due to the smaller grains [9
]. However, yield losses can also be caused by diseases. Weather conditions can favor infections. Typical wheat diseases include Blumeria graminis, Septoria triticii, Puccinia striiformis, and Fusarium graminearum [16
1.2. Objectives of this Study
Yield sensitivity to basic meteorological variables (temperature means, temperature sums, and precipitation sums) has been studied extensively through regression analyses [17
]. However, only focusing on these simple parameters ignores variables and indices and their influence on yields. Therefore, in this study, we included more parameters in the modelling. For this purpose, individual parameters and indices were identified from the literature [23
]. We mapped the whole growing season for these parameters and not only the main vegetation period.
To examine these connections, we focused on the influence of these climate variables on yields using qualitative and quantitative calculations.
Additionally, this study evaluates the consequences of reduced nitrogen fertilization. The Fertilizer Ordinance, which came into force in Germany last year, requires reduced N application in certain areas with excessive NO3 values in groundwater. However, there are no clear findings on the effects of this reduced fertilization on yields. Here, this experiment provides essential insights into such a reduction depending on varieties, management and also climate conditions.
Considering the biological implications of the plant development determined by the climate conditions, however, was not the objective here.
Our hypothesis was that the long-term trend in yield series is mainly due to progress in breeding, diseases, and technical advancement, whereas the short-term inter-annual yield variations are more described by meteorological parameters.
Literature overview of various studies investigating the influence of climate on grain yield.
Literature overview of various studies investigating the influence of climate on grain yield.
|Author ||Location ||Crop ||Described Factors of Influence|
|||Germany||Wheat, barley, ||Yield fluctuations of wheat and barley are mainly caused by precipitation and temperature in June in selected federal states of Germany|
|||Germany||Wheat, barley, ||The influence of precipitation and temperature on the yield development of wheat, barley and maize in selected districts in Bavaria with special consideration of development stages|
|||Germany||Wheat ||Development of heat and drought events and the change in wheat yield |
| ||Germany ||Wheat ||Influence of temporary waterlogging on growth, nutrient concentration, and yield of wheat|
|||Denmark ||Wheat ||Effect of average temperature, global radiation, and daily precipitation on wheat yields in Denmark (over 17 years)|
|||Europe ||Wheat ||Effect of mean monthly temperature, global radiation, and cumulative rainfall on the yields of winter wheat (over 34 years)|
|||Europe ||Wheat, barley ||Temperature explains most of the yield fluctuations in Europe|
|||Canada||-||Effects of climate change and CO2 increase on potential agricultural production in Southern Québec, Canada|
|||Mexico||Wheat||Mexico: 25% increase in wheat yield in the last two decades due to higher night temperatures|
|||USA||Spring wheat||Impacts of day versus night temperatures on spring wheat yields: A comparison of empirical and CERES wheat 2.0 model predictions in three locations|
|||USA||Wheat||Simulating the influence of vernalization, photoperiod, and optimum temperature on wheat developmental rates|
|||USA||Wheat, maize||Sensitivity of seeds to brief episodes of hot temperatures (e.g., flowering)|
|||Europe ||Wheat ||Sensitivity of wheat varieties grown in Europe to heat, drought, frost, and precipitation|
|||India||Wheat ||Effect of lack of water on the yield of winter wheat|
|||Australia ||Wheat ||Influence of temperature increases on the yield|
|||Australia||Wheat||The effect of duration of heat stress during grain filling on two wheat varieties differing in heat tolerance: grain growth and fractional protein accumulation|
|||China ||Wheat ||Influence of frost on yield in the jointing stage|
|||China ||Wheat ||Influence of heat on the grain filling phase in wheat|
|||-||Cereal||Influence of heat on different stages of development of the reproductive phase in different cereal varieties |
|||-||Wheat ||Summary of frost and heat damage models that can estimate the impact on the yield|
|||-||Wheat ||Summary of optimal and lethal temperatures of wheat during different stages of development|
|||-||Wheat ||Influence of waterlogging in different growth phases on the yield|
The occurrence of winter indicators was noticeable in the monthly and annual values, especially on the unfertilized variants and, to a lesser extent, in the calculations of fertilized yields. One reason for this is the soil conditions. The available field capacity of this site is around 241 mm up to 100 cm of soil depth (according to [38
]), this is classified as high) and 325 mm up to 140 cm soil depth. At the beginning of the main vegetation season in March/April, the maximum amount of available field water capacity is reached, so a wheat stand can grow almost completely without irrigation. The soil water content is also temporarily improved by the addition of slope water. The precipitation therefore does not play a direct role in the calculation. In the low and high fertilization levels, however, the climate variable of precipitation-free days in June was significant. The standardized β coefficients showed that the importance of the precipitation-free days in June increased with increasing fertilization, likely due to the higher biomass yield and the associated increase in water consumption of the crop. A further important factor is the relief in the areas. The experimental field is located in a small valley, which allows cold air to accumulate temporarily. The outflow of cold air is delayed by the downhill forest stand. Overall, the predictors of the monthly values led to adjusted R2
values of at least 0.7.
The annual figures (Figure 4
) showed a weaker relationship. The adjusted R2
values were 0.4 for the unfertilized control and low fertilization, and 0.65 for the highly fertilized plots. Winter values were also significant here (EFI and WI). In the following, the significant climate variables are presented.
In 1985, the first days with frost occurred in October. The wheat was sown at the beginning of October, so the phase of emergence was influenced by frost. In the regression analysis, the early frost index proved to be significant. However, we were unable to determine whether early frost was a restriction in all growing years, as the sowing rate fluctuates between early October and early November. If the emergence of wheat is not finished before the first days of frost, wintering damage may occur as the wheat grain is very sensitive to frost [45
] (p. 318). Additionally, the winter index, temperature threshold in December and February also proved to be significant in the regression analysis.
For the transition from the vegetative to the generative phase, temperatures around the 3–4 °C are necessary for a longer period of time. Vernalization removes shooting inhibition and the plants enter the generative phase and begin to grow in length. If this cold stimulation does not occur, the plants remain in the vegetative phase [45
]. In the same phase, however, most frost damage occurs at low temperatures as the plants are very sensitive in this phase and the vegetation cone is no longer protected by the leaf sheath close to the soil surface [11
]. In 1983 and 1986, which experienced constant frost in February, the wheat was affected by the frost.
Here, the winter hardiness of the plants was found to be responsible for the yield losses. The extent to which a plant is damaged by frost depends on the temperatures, the duration of frost events, the insulating effect of a layer of snow, the stage of development, and the variety [10
Infection with pathogens of individual wheat diseases usually occurs in the months of May and June in appropriate weather conditions. In 1983, 1995, 1998, 2007, and 2010, infections with Puccinia striiformis, Blumeria graminis f. sp. tritici, Septoria tritici, and Fusarium graminearum were possible due to the wetness. Late sowing of winter wheat is a strategy to prevent the occurrence of these diseases. Other strategies include the avoidance of excess nitrogen, the cultivation of resistant or tolerant varieties, clean soil treatment to combat volunteer grain, and fungicide treatment. [46
] investigated yield losses of winter wheat from 2003 to 2008 in trials of 12 German federal states. Here, the highest yield losses were caused by Septoria leaf blotch (Mycosphaerella graminicola) with yield reductions of about 7 dt ha−1
. Brown rust (Puccinia triticina) also occurred in all German federal states and were second most important in both occurrence and yield loss [47
]. No surveys of this kind were conducted on the trial site investigated here. However, losses cannot be ruled out.
A drought in June also negatively influenced the yield. In the regression analysis, the rain-free days in June proved to be significant. Based on long-term records, during this period wheat is in the phase of flowering or grain filling. The optimum temperature for these development stages is 21 °C [37
]. To avoid heat stress, different plant responses are known (shortened life cycle, a higher rate of phenological development, and a more efficient use of reserves [14
]). However, a shortened life cycle means that the thousand-grain weight is reduced, since fewer assimilates can be stored in the grain, which leads to a reduction in yield.
The plant can also try to minimize water loss and maximize water uptake through improved root growth or premature senescence of older leaves [12
]. Due to the premature senescence of the older leaves, not all assimilates can be stored in the grain, which leads to a lower thousand grain weight and thus to a lower yield.
Whether the plant has really been affected by the drought is difficult to judge. As already mentioned, the soil at the experimental station has a high usable field capacity due to the high silt and soil carbon content. If the soil water supply is well-filled during the winter months, the plant can also produce good yields with less rainfall. However, no conclusion can be drawn about the soil water supply in June, as the necessary information was not considered. Excessive precipitation during this period can also negatively impact yield. If the precipitation is too high, the water can no longer flow off unhindered, so it accumulates. This waterlogging causes abiotic stress in the plants. Stagnant moisture can lead to premature ripening of the wheat in the phases of grain shifting (initiation of individual flowers) and flowering, which results in a shortened grain filling phase and thus to a yield reduction due to lower thousand grain weights [9
Due to the high silt content, the soil at the experimental station also tends to silt. Due to the silting, the rainwater can no longer infiltrate and flows off. Erosion occurs [48
In summary, the modelling showed a balanced relationship between complexity and robustness, which minimizes both structural- and parameter-related errors. If the concept of location is assumed, these calculations can also be improved. In addition to weather conditions, relief and soil can be included.
In addition to the chosen statistical method, there is also the possibility of using artificial intelligence methods. Here, possible non-linearities contained in the relationships between climate and yields can also be taken into account.
Earlier studies showed that wheat yields can be well represented by topographic parameters and soil conductivity. However, the annual range was variable (unfertilized: R2
of 0.76–0.95, in 1980, 1983, 1989, 1995, 1998, 2001, 2004, 2007, and 2012 and R2
of 0.46–0.66 in 1986, 1992, and 2010) [39
For farming, the results can be used to make appropriate management decisions about seeding, fertilization, and irrigation [49
]. During the actual growing season, climate data are available the day prior. The knowledge of the growing stage, the current climate, and the results of the regressions above provide the opportunity to react, for example, with the fertilization. For example, if temperature threshold days during late emergence or in April are met, N fertilization should be reduced.
The aim of this work was to determine the influence of climate parameters on the wheat yield grown in one of the more fertile areas in Germany. Not all aspects of climate that account for yield differences can be assayed with monthly or annual indices as used in this study. In this study, the short-term influencing factors were derived by subtraction of the smoothing yield development from the measured yield. The remaining residuals indicated the short-term inter-annual variation, which were determined by the meteorological factors.
However, these approaches provide an extended comprehensive analysis of dedicated yield assessments as done on this site. For the monthly values, the rain-free days in June and the temperature thresholds in April, February, and December were significant. For the annual values, the climate humidity, the early frost index, and the winter index were significant.
The use of such climate parameters in the calculations differed from other analyses where only directly measured parameters were used, like temperature, precipitation, and global radiation [43
The results indicate a changed picture of the yield development of this fertile location. The findings that frost and temperature fluctuations are relevant to yields, especially in the transition period from winter to warmer seasons and vice versa, had not been previously described.
A further interesting observation could be made on this site. Different levels of nitrogen fertilization modified the sensitivity to drought and temperature in the case of monthly calculations. The contrary effect of temperature threshold and days without precipitation indicated that higher fertilized crops reacted more sensitively to the impact of drought and less to the temperature threshold. This finding is remarkable, since the difference in yield between high and low fertilized, was only 2–3 dt ha−1 on a multi-annual average due to a reduction of fertilizing by 20–30%. In other words, 20–30% less fertilization reduced the sensitivity to a lack of precipitation on this site. However, this hypothesis needs further verification.
However limitation of this regression should be further considered. The current calculation is only valid for the respective site with its unique climate situation, soil data and applied farming practice. The climate data represent only by a nearby weather station, which can only provide selective information. However, it may be prudently concluded that the same conclusions probably apply on a regional level.
This regression was not constructed for the testing of the effects of climate change. The listed calculations are black box models, which imitate reactions without knowing the effect of the individual influencing parameters. Here, it is sufficient that the model shows the same behavior as the original. The black box modelling is empirical, meaning it is based on observations of behavior (input and output data). The advantage of this type of modelling is that complex (sometimes even impossible) causal analyses can be inferred and powerful and computer-intensive methods are available for this purpose. The application of such analyses have become increasingly accepted. Accurate climate data are essential for input data. These measurements should be recorded where agricultural production is an economic source of income [49