Growing degree hours - a simple, accurate, and precise protocol to approximate growing heat summation for grapevines

Growing degree-hours and degree-days in two management zones for each phenological stage of wheat (Triticum aestivum L.)

Monitoring the climatic conditions of crops is essential for smart agriculture development and adaptation of agricultural systems in the era of global change. Thereby, it is possibly better to understand the stages of development of the crop, thus adopting management practices more efficiently and planning the harvest with greater accuracy. This study was developed to analyze the growing degree-hours and degree-days in two management zones (MZs) for each phenological stage of wheat (Triticum aestivum L.) and the application of low-cost agroclimatological stations to monitor the climatic conditions of the field production. The study was developed in a Ferralsol in Céu-Azul/Brazil. Ten low-cost agrometeorological stations were installed in two MZs delineated based on elevation data using the web platform AgDataBox. Data on solar radiation, atmospheric pressure, wind speed, precipitation, relative humidity, air, and soil temperature were evaluated over two wheat crop seasons. Our results showed different climatic conditions, especially humidity and temperature, between MZs and crop seasons, which could probably cause yield variability. By the low-cost agroclimatological stations, it is possible to collect data on the thermal accumulation by the culture in growing degree-hours, which is a more accurate parameter than the growing degree-days (commonly used in similar studies). With the growing degree-hours data, it was possible to follow the development of the phenological stages of wheat. In conclusion, the results obtained suggest the importance of evaluating agroclimatological parameters in monitoring wheat crops. However, more studies are needed in regions with greater slopes, which may have microclimates that intensely influence the crop.

Limits on phenological response to high temperature in the Arctic

Tundra plants are widely considered to be constrained by cool growing conditions and short growing seasons. Furthermore, phenological development is generally predicted by daily heat sums calculated as growing degree days. Analyzing over a decade of seasonal flower counts of 23 plant species distributed across four plant communities, together with hourly canopy-temperature records, we show that the timing of flowering of many tundra plants are best predicted by a modified growing degree day model with a maximum temperature threshold. Threshold maximums are commonly employed in agriculture, but until recently have not been considered for natural ecosystems and to our knowledge have not been used for tundra plants. Estimated maximum temperature thresholds were found to be within the range of daily temperatures commonly experienced for many species, particularly for plants at the colder, high Arctic study site. These findings provide an explanation for why passive experimental warming-where moderate changes in mean daily temperatures are accompanied by larger changes in daily maximum temperatures-generally shifts plant phenology less than ambient warming. Our results also suggest that many plants adapted to extreme cold environments may have limits to their thermal responsiveness.

Effect of weather conditions, substrate pH, biochar amendment and plant species on two plant growth-promoting microbes on vegetated roofs and facades

Background

Vegetated building envelopes (VBEs), such as vegetated roofs and facades, are becoming more frequent in urban planning nowadays. However, harsh growing conditions restrain the application of VBEs. Plant growth-promoting microbes (PGPMs) might help ease the stresses, but first, it is necessary to investigate how to ensure their survival and growth under VBE conditions.

Methods

We conducted three experiments to test the impact of various factors on the microbial populations of inoculated PGPMs in VBEs, a mycorrhizal fungus Rhizophagus irregularis and a bacterium Bacillus amyloliquefaciens. The first experiment was conducted by inoculating the two PGPMs separately in Sedum roof plots, and the microbial populations associated with Poa alpina was monitored for two consecutive years under local weather conditions. The second experiment was conducted in a laboratory testing the effect of substrate pH (substrates collected from balcony gardens) on R. irregularis population associated with Trifolium repens and Viola tricolor. The third experiment was conducted on a meadow roof testing the effect of biochar amendment on R. irregularis population associated with Thymus serpyllum and Fragaria vesca.

Results

In the first experiment, Bacillus was found to associate with P. alpina, but Rhizophagus wasn't. Yet, the fungus induced high Bacillus population density in the Rhizophagus treated plots in the first year. In the second experiment, Rhizophagus abundance in T. repens was higher in the neutral substrate (6–6.5), while V. tricolor was more colonized in acidic substrate (5–5.5), suggesting an important interactive effect of substrate pH and plant species on Rhizophagus abundance. The third experiment suggested a negligible impact of biochar amendment on Rhizophagus abundance for both host plants.

Conclusion

Three experiments demonstrate that PGPM inoculation on VBEs is feasible, and various factors and interactions affect the PGPM populations. This paper provides reference and inspiration for other VBE research involving substrate microbial manipulation.

Towards a Stochastic Model to Simulate Grapevine Architecture: A Case Study on Digitized Riesling Vines Considering Effects of Elevated CO2

Modeling plant growth, in particular with functional-structural plant models, can provide tools to study impacts of changing environments in silico. Simulation studies can be used as pilot studies for reducing the on-field experimental effort when predictive capabilities are given. Robust model calibration leads to less fragile predictions, while introducing uncertainties in predictions allows accounting for natural variability, resulting in stochastic plant growth models. In this study, stochastic model components that can be implemented into the functional-structural plant model Virtual Riesling are developed relying on Bayesian model calibration with the goal to enhance the model towards a fully stochastic model. In this first step, model development targeting phenology, in particular budburst variability, phytomer development rate and internode growth are presented in detail. Multi-objective optimization is applied to estimate a single set of cardinal temperatures, which is used in phenology and growth modeling based on a development days approach. Measurements from two seasons of grapevines grown in a vineyard with free-air carbon dioxide enrichment (FACE) are used; thus, model building and selection are coupled with an investigation as to whether including effects of elevated CO2 conditions to be expected in 2050 would improve the models. The results show how natural variability complicates the detection of possible treatment effects, but demonstrate that Bayesian calibration in combination with mixed models can realistically recover natural shoot growth variability in predictions. We expect these and further stochastic model extensions to result in more realistic virtual plant simulations to study effects, which are used to conduct in silico studies of canopy microclimate and its effects on grape health and quality.

Assessment of Climate Change Impacts on Chilling and Forcing for the Main Fresh Fruit Regions in Portugal

Air temperature plays a major role in the growth cycle of fruit trees. Chilling and forcing are two of the main mechanisms that drive temperate fruit development, namely dormancy and active plant development. Given the strong sensitivity of these crops to air temperature and the foreseeable warming under future climates, it becomes imperative to analyze climate change impacts for fruit trees. The fruit sector in Portugal has risen significantly over the last decades, gaining increasing importance both internally and through exports. The present research assesses the impacts of climate change on the chilling and forcing for economically relevant fruit trees in Portugal, namely apples, oranges, pears, and plums. To assess temperate fruit chilling and forcing conditions, the chilling portions (CP) and growing degree-hours (GDH) were computed over Portugal, for the recent-past (1989-2005) and future (2021-2080) periods, following two anthropogenic radiative forcing scenarios (RCP4.5 and RCP8.5). Future climate data were obtained from four regional-global climate model pairs to account for model uncertainties. Bias-correction methodologies were also applied. A spatial analysis over the main regions with PDO "Protected Denomination of Origin" or PDI "Protected Geographical Indication" of origin of each fruit tree was performed. Future projections show a clear decrease in chilling for all regions and fruit types in Portugal. Nonetheless, given the current chilling values in Portugal and the relative importance of chilling accumulation for each fruit type, these changes are more significant for certain varieties of apples than for other types of fruit. Regarding forcing, the future projections highlight an increase in its values throughout the different fruit tree regions in Portugal, which should lead to earlier phenological timings. These changes may bring limitations to some of the most important Portuguese temperate fruit regions. The planning of suitable adaptation measures against these threats is critical to control the risk of exposure to climate change, thus warranting the future sustainability of the Portuguese fruit sector, which is currently of foremost relevance to the national food security and economy.

The problem of scale in predicting biological responses to climate

Many analyses of biological responses to climate rely on gridded climate data derived from weather stations, which differ from the conditions experienced by organisms in at least two respects. First, the microclimate recorded by a weather station is often quite different to that near the ground surface, where many organisms live. Second, the temporal and spatial resolutions of gridded climate datasets derived from weather stations are often too coarse to capture the conditions experienced by organisms. Temporally and spatially coarse data have clear benefits in terms of reduced model size and complexity, but here we argue that coarse-grained data introduce errors that, in biological studies, are too often ignored. However, in contrast to common perception, these errors are not necessarily caused directly by a spatial mismatch between the size of organisms and the scale at which climate data are collected. Rather, errors and biases are primarily due to (a) systematic discrepancies between the climate used in analysis and that experienced by organisms under study; and (b) the non-linearity of most biological responses in combination with differences in climate variance between locations and time periods for which models are fitted and those for which projections are made. We discuss when exactly problems of scale can be expected to arise and highlight the potential to circumvent these by spatially and temporally down-scaling climate. We also suggest ways in which adjustments to deal with issues of scale could be made without the need to run high-resolution models over wide extents.

Estimation of daily mean temperatures: an accurate method for the Douro Valley

Air temperature data from many locations worldwide are only available as series of daily minima and maxima temperatures. Historically, several different approaches have been used to estimate the actual daily mean temperature, as only in the last two or three decades automatic thermometers are able to compute its actual value. The most common approach is to estimate it by averaging the daily minima and maxima. When only daily minima and maxima are available, an alternative approach, proposed by Dall’Amico and Hornsteiner in 2006, uses the two daily extremes together with next day minima temperature and a coefficient related to the local daily astronomical sunset time. Additionally, the method uses two optimizable coefficients related to the region’s temperature profile. In order to use this approach it is necessary to optimize the region’s unknown parameters. For this optimization, it is necessary a dataset containing the maxima, minima, and the actual daily mean temperatures for at least one year. In this research, for the period 2007-2014, we used three datasets of minima, maxima and actual mean temperatures obtained at three automatic meteorological stations located in the Douro Valley to optimize the two unknown parameters in the Dall’Amico and Hornsteiner approach. Moreover, we compared the actual mean daily temperatures available from the three datasets with the correspondent values estimated by using i) the usual approach of averaging the daily maxima and minima temperatures and ii) the Dall’Amico and Hornsteiner approach. Results show that the former approach overestimates, on average, the daily mean temperatures by 0.5ºC. The Dall’Amico and Hornsteiner approach showed to be a better approximation of mean temperatures for the three meteorological stations used in this research, being unbiased relative to the actual mean values of daily temperatures. In conclusion, this research confirms that the Dall’Amico and Hornsteiner is a better approach to estimate the mean daily temperatures and provides the optimized parameters for three sites located at each of the three sub-regions of the Douro Valley (Baixo Corgo, Cima Corgo and Douro Superior).

Generic calibration of a simple model of diurnal temperature variations for spatial analysis of accumulated degree-days

Accumulated growing degree-days (aGDD) are widely used to predict phenological stages of plants and insects. It has been shown in the past that the best predictive performance is obtained when aGDD are computed from hourly temperature data. As the latter are not always available, models of diurnal temperature changes are often employed to retrieve the required information from data of daily minimum and maximum temperatures. In this study, we examine the performance of a well-known model of hourly temperature variations in the context of a spatial assessment of aGDD. Specifically, we examine whether a generic calibration of such a temperature model is sufficient to infer in a reliable way spatial patterns of key phenological stages across the complex territory of Switzerland. Temperature data of a relatively small number of meteorological stations is used to obtain a generic model parameterization, which is first compared with site-specific calibrations. We show that, at the local scale, the predictive skill of the generic model does not significantly differ from that of the site-specific models. We then show that for aGDD up to 800 °C d (on a base temperature of 10 °C), phenological dates predicted with aGDD obtained from estimated hourly temperature data are within ± 3 days of dates estimated on the basis of observed hourly temperatures. This suggests the generic calibration of hourly temperature models is indeed a valid approach for pre-processing temperature data in regional studies of insect and plant phenology.

Climate change impacts and adaptive strategies: lessons from the grapevine

The cultivation of grapevines for winemaking, known as viticulture, is widely cited as a climate-sensitive agricultural system that has been used as an indicator of both historic and contemporary climate change. Numerous studies have questioned the viability of major viticulture regions under future climate projections. We review the methods used to study the impacts of climate change on viticulture in the light of what is known about the effects of climate and weather on the yields and quality of vineyard harvests. Many potential impacts of climate change on viticulture, particularly those associated with a change in climate variability or seasonal weather patterns, are rarely captured. Key biophysical characteristics of viticulture are often unaccounted for, including the variability of grapevine phenology and the exploitation of microclimatic niches that permit successful cultivation under suboptimal macroclimatic conditions. We consider how these same biophysical characteristics permit a variety of strategies by which viticulture can adapt to changing climatic conditions. The ability to realize these strategies, however, is affected by uneven exposure to risks across the winemaking sector, and the evolving capacity for decision-making within and across organizational boundaries. The role grape provenance plays in shaping perceptions of wine value and quality illustrates how conflicts of interest influence decisions about adaptive strategies within the industry. We conclude by considering what lessons can be taken from viticulture for studies of climate change impacts and the capacity for adaptation in other agricultural and natural systems.