Growing Degree Days Research Articles

Spatiotemporal patterns of microclimatic buffering in relict alpine communities

AbstractQuestionsIn alpine landscapes, topography creates a mosaic of microclimatic niches that might prevent local extinctions, but the influence of this spatial heterogeneity on plant communities is largely unknown. Here we ask (1) how soil microclimatic variation is comparable at temporal and spatial scales, and (2) how such variation influences species composition and local extinctions in relict alpine communities.LocationPicos de Europa National Park, northern Spain.MethodsWe resurveyed permanent plots in four alpine sites following the recording of soil temperatures (temporal survey) for 10 years. We then sampled the spatial variation in species composition and microclimatic temperatures in 80 plots around the permanent plots (spatial survey). We evaluated the variation of six microclimatic indices between the temporal and the spatial surveys, and calculated the temporal trends observed in species cover. We finally predicted local extinction rates under microclimatic scenarios based on the observed microclimate–community relations.ResultsDespite high interannual variation, we found a 10‐year trend of temperature warming on (microridge) fellfields and (microvalley) snowbeds. Microclimatic variation was larger in space than in time, with little temperature variation in snowbeds and extreme low temperatures recorded in fellfields. Species composition was mainly influenced by growing degree days (GDD) and freezing degree days (FDD), which were both related to snow cover duration. Plant cover of 16 species (out of 36 frequent species) showed significant responses to microclimatic variation. Local extinctions were mainly predicted under relatively hotter and more freezing conditions.ConclusionsOur results support the idea that microclimatic spatial heterogeneity can reduce the negative influence of climate change on alpine plant communities. However, a continuous reduction of snow cover will result in a tipping point beyond which the buffer effect of this spatial heterogeneity will not be effective in protected microsites, leading to community homogenization. This process may have started in relict alpine communities where species from snowy microclimates are being outcompeted by species adapted to below‐zero winter temperatures.

Effect of Microclimate Modification on Growth and Yield of Basmati Rice under Punjab Conditions

The field experiments were conducted at the Research Farm, Department of Climate Change and Agricultural Meteorology, Punjab Agricultural University, Ludhiana during kharif 2018. Pusa Basmati variety was transplanted on 5th July (D1) and 15th July (D2) under three spacing (25 cm x12 cm, 20 cm x 15 cm and 30 cm x 10 cm). The micrometeorological data on photosynthetically active radiation (PAR) was recorded at different phenological stages. The periodic biometric observations on leaf area index were recorded. Growing degree days (GDD) were calculated at different phenological stages. The results indicate that PAR interception and leaf area index was higher under wider spacing (30 cm x 10 cm) followed by closer spacing (20 cm x 15 cm and 25 cm x 12 cm). The leaf area index was higher under wider spacing (30 cm x 10 cm) followed by closer spacing (20 cm x 15 cm and 25 cm x 12 cm). Correlation coefficients were worked out between periodic number of tillers of rice and temperature (maximum and minimum) and sunshine hours/day. In both dates of transplanting, maximum temperature showed a significant negative correlation with tiller number in most of the cases. The sunshine hours in both dates of transplanting showed a positive and significant correlation under different treatments. The first date of transplanting reported higher grain yield (30.61 q/ha) as compared to second date of transplanting (27.20 q/ha). The higher grain yield (30.57 q/ha) was recorded in basmati rice transplanted under wider spacing (30 cm x 10 cm) in comparison to closer spacing of 20 cm x 15 cm (29.25 q/ha) and 25 cm x 12 cm (26.89 q/ ha). Hence, microclimate modification through dates of transplanting and spacing is cost effective measure for increasing basmati rice yield.

Assessing relationships of cover crop biomass and nitrogen content to multispectral imagery

AbstractCover crops provide valuable roles in sustainable agriculture, provided they produce enough biomass. To accurately measure their services to field management, spatial estimates would be useful to producers. This study used multispectral drone imagery to produce maps of normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and a digital surface model (DSM) of cover crop plots on sandy, Mid‐Atlantic soils. Cover crops included cereal rye (Secale cereale), mixtures of rye and crimson clover (Trifolium incarnatum), and mixtures of rye and hairy vetch (Vicia villosa). Their biomass was sampled in the spring of 2019, 2020, and 2021, dried, weighed, and analyzed for total nitrogen (N) content. Measurements of NDVI became saturated (i.e., reached a linear plateau) at 3.86 Mg biomass ha−1, NDRE at 5.72 Mg biomass ha−1, and the DSM at 5.11 Mg biomass ha−1. The measured N content became saturated at 80.9, 139.1, and 75 kg N ha−1 for NDVI, NDRE, and the DSM, respectively. Based on log transformations, NDVI was a stronger predictor of biomass and N, but not C:N. The NDRE was important for biomass, N, and C:N, while the DSM interactions with cover crop species helped predict both the N content and C:N of cover crop tissues. Accumulated growing degree days was important as an individual variable for biomass and N and as an interaction with cover crop species.

Combining Recurrent Neural Network and Sigmoid Growth Models for Short-Term Temperature Forecasting and Tomato Growth Prediction in a Plastic Greenhouse

Compared with open-field cultivation, greenhouses can provide favorable conditions for crops to grow through environmental control. The prediction of greenhouse microclimates is a way to reduce environmental monitoring costs. This study used several recurrent neural network models, including long short-term memory (LSTM), gated recurrent unit, and bi-directional LSTM, with varying numbers of hidden layers and units, to establish a temperature forecasting model for a plastic greenhouse. To assess the generalizability of the proposed model, the most accurate forecasting model was used to predict the temperature in a greenhouse with different specifications. During a test period of four months, the best proposed model’s R2, MAPE, and RMSE values were 0.962, 3.216%, and 1.196 °C, respectively. Subsequently, the outputs of the temperature forecasting model were used to calculate growing degree days (GDDs), and the predicted GDDs were used as an input variable for the sigmoid growth models to simulate the leaf area index, fresh fruit weight, and aboveground dry matter of tomatoes. The R2 values of the growth model for the three growth traits were all higher than 0.80. Moreover, the fitted values and the parameter estimates of the growth models were similar, irrespective of whether the observed GDD (calculated using the actual observed data) or the predicted GDD (calculated using the temperature forecasting model output) was used. These results indicated that the proposed temperature forecasting model could accurately predict the temperature changes inside a greenhouse and could subsequently be used for the growth prediction of greenhouse tomatoes.

Advanced Farming Strategies Using NASA POWER Data in Peanut-Producing Regions without Surface Meteorological Stations

Understanding the impact of climate on peanut growth is crucial, given the importance of temperature in peanut to accumulate Growing Degree Days (GDD). Therefore, our study aimed to compare data sourced from the NASA POWER platform with information from surface weather stations to identify underlying climate variables associated with peanut maturity (PMI). Second, we sought to devise alternative methods for calculating GDD in peanut fields without nearby weather stations. We utilized four peanut production fields in the state of Georgia, USA, using the cultivar Georgia-06G. Weather data from surface stations located near peanut fields were obtained from the University of Georgia’s weather stations. Corresponding data from the NASA POWER platform were downloaded by inputting the geographic coordinates of the weather stations. The climate variables included maximum and minimum temperatures, average temperature, solar radiation, surface pressure, relative humidity, and wind speed. We evaluated the platforms using Pearson correlation (r) analysis (p < 0.05), linear regression analysis, assessing coefficient of determination (R2), root mean square error (RMSE), and Willmott index (d), as well as principal component analysis. Among the climate variables, maximum and minimum temperatures, average temperature, and solar radiation showed the highest R2 values, along with low RMSE values. Conversely, wind speed and relative humidity exhibited lower correlation values with errors higher than those of the other variables. The grid size from the NASA POWER platform contributed to low model adjustments since the grid’s extension is kilometric and can overlap areas. Despite this limitation, NASA POWER proves to be a potential tool for PMI monitoring. It should be especially helpful for growers who do not have surface weather stations near their farms.

Doubled Haploid Lines Derived from a European Maize Flint Landrace Contrast in Recovery from Cold Stress

Suboptimal temperatures at sowing and emergence affect the early development of maize, with potentially irreversible effects later in the growing season. We studied recovery from cold stress of an inbred line (B73) and 13 Doubled Haploid lines derived from a European flint maize landrace. After a cold treatment (20–12 °C, day–night) from sowing to seedling establishment, seedlings were transplanted and grown in the greenhouse until the V8 stage (eight leaves fully developed), when we measured agronomically relevant plant traits and spectral indices of mature leaves. Survival rates of transplanted seedlings after cold treatment ranged from 10% to 100%. After a strong delay in early development due to cold, the surviving plants were able to compensate for this delay at later stages of recovery. They reached the V8 stage after only five more growing degree days than plants grown under the control treatment (25–18 °C, day–night). Plants from the most cold-tolerant genotypes (PE0401 and PE0100) accumulated more root and shoot biomass at the end of the recovery phase compared with the same genotypes exposed to the control treatment. The genotypes with the most plastic leaf morphological traits (PE0161 and PE0072) had little reduction in leaf biomass at the end of the recovery phase in comparison with less responsive genotypes such as PE0171. We conclude that genotypes that survived cold treatment with minimal cold damage of seedling leaves can be candidates for further cold recovery studies and breeding. Nevertheless, such studies must take trait acclimation for other suboptimal conditions into consideration.

Early‐season biomass and weather enable robust cereal rye cover crop biomass predictions

AbstractFarmers need accurate estimates of winter cover crop biomass to make informed decisions on termination timing or to estimate potential release of nitrogen from cover crop residues to subsequent cash crops. Utilizing data from an extensive experiment across 11 states from 2016 to 2020, this study explores the most reliable predictors for determining cereal rye cover crop biomass at the time of termination. Our findings demonstrate a strong relationship between early‐season and late‐season cover crop biomass. Employing a random forest model, we predicted late‐season cereal rye biomass with a margin of error of approximately 1,000 kg ha−1 based on early‐season biomass, growing degree days, cereal rye planting and termination dates, photosynthetically active radiation, precipitation, and site coordinates as predictors. Our results suggest that similar modeling approaches could be combined with remotely sensed early‐season biomass estimations to improve the accuracy of predicting winter cover crop biomass at termination for decision support tools.Core Ideas Cereal rye winter cover crop biomass modeled on data from 35 site‐years. We found a strong relationship between early and late‐season biomass. Random forest model with early‐season biomass and weather data performed well. Similar approach could improve decision support tools for cover crop management.

Deciphering temporal growth patterns in maize: integrative modeling of phenotype dynamics and underlying genomic variations.

Quantifying the temporal or longitudinal growth dynamics of crops in diverse environmental conditions is crucial for understanding plant development, requiring further modeling techniques. In this study, we analyzed the growth patterns of two different maize (Zea mays L.) populations using high-throughput phenotyping with a maize population consisting of 515 recombinant inbred lines (RILs) grown in Texas and a hybrid population containing 1090 hybrids grown in Missouri. Two models, Gaussian peak and functional principal component analysis (FPCA), were employed to study the Normalized Green-Red Difference Index (NGRDI) scores. The Gaussian peak model showed strong correlations (c. 0.94 for RILs and c. 0.97 for hybrids) between modeled and non-modeled temporal trajectories. Functional principal component analysis differentiated NGRDI trajectories in RILs under different conditions, capturing substantial variability (75%, 20%, and 5% for RILs; 88% and 12% for hybrids). By comparing these models with conventional BLUP values, common quantitative trait loci (QTLs) were identified, containing candidate genes of brd1, pin11, zcn8 and rap2. The harmony between these loci's additive effects and growing degree days, as well as the differentiation of RIL haplotypes across growth stages, underscores the significant interplay of these loci in driving plant development. These findings contribute to advancing understanding of plant-environment interactions and have implications for crop improvement strategies.

Regional detection and assessment of chilling damage on maize considering land surface temperature, crop growth status and solar radiation changes

AbstractIncreased frequency and severity of chilling damage events pose potential risks to crop performance and productivity due to climate change. Accurate and real‐time access to chilling damage is important for crop growth and yield stability based on field's actual environment. To precisely identify regional chilling events and evaluate the impacts on crops, this study presents a model to estimate field air temperature in view of field crop situations. Land surface temperature, enhanced vegetation index, solar‐induced chlorophyll fluorescence and solar declination were involved in the model. With field simultaneous continuous monitoring and multisource fused remote sensing data, the model was calibrated and validated in Jiefangzha Irrigation Area (JIA) and Changchun City (CC) in North China, accompanied by the determination coefficient ≥0.756, root mean square error ≤0.782°C, relative error ≤0.041 and consistency index ≥0.902. Meanwhile, sensitivities of the model factors were determined through path analysis, where the factors performed according to the order solar‐induced chlorophyll fluorescence >solar declination >land surface temperature > enhanced vegetation index. Using the validated model, chilling damage to maize was further detected in JIA and CC from 2010 to 2020. Results showed that the severity of chilling damage was greater in CC than in JIA, along with the sterile‐type occurring three events in JIA and seven in CC, while the delayed‐type only twice in JIA in 2012 and 2016, but five times in CC in 2013, 2014, 2016, 2017 and 2019, respectively, being consistent with local statistics. In response to chilling damage, enhanced vegetation index and solar‐induced chlorophyll fluorescence demonstrated the negative chilling effects on greenness and light use efficiency for fluorescence. Serious yield losses were caused, with yield‐reducing by 5.00% (Dehui, 2013), 19.00% (Jiutai, 2014), 21.65% (Suburban district, 2016), 8.83% (Shuangyang, 2017) and 2.19% (Jiutai, 2019) in CC. The linear relationship between yield and growing degree days was a bit weakened by chilling damage, with the determination coefficient varying from 0.614 to 0.531. The increasing rate of yield with growing degree days decreased from 20.365 kg/(°C·d) in non‐chilling damage years to 9.670 kg/(°C·d) in chilling damage years. These findings indicate that the presented model is especially adaptive for agricultural field environments, enabling rapid precision detection of chilling damage on crops at regional scales. It will provide references for gauging the impact of chilling damage on crops, finding efficient solutions to the stress and ensuring sustainable development of agriculture.

Thresholds and extent of temperature effects on maize yield differ in different grain-filling stages

Temperature is a vital environmental factor affecting grain filling and maize yield. The response of maize yield to temperature at different stages of grain filling, however, remains uncharacterized. This study used “Zhengdan 958” as the test material to analyze the high-temperature threshold and yield sensitivity of grain-filling in different periods without water stress by using the data from staging sowing experiments at agro-meteorological experimental stations in Hebi and Suzhou in the Huang-Huai-Hai Plain from 2019 to 2022. The results demonstrated that: (1) the maximum temperature threshold was different in various periods of maize grain-filling in the Huang-Huai-Hai Plain, showing the early grain-filling period (EP) > the active grain-filling period (AP) > the late grain-filling period (LP). With the largest differences in temperature thresholds found in AP, the maximum temperature threshold of AP can better reflect the characteristics of grain filling rather than the whole filling period. (2) The heat of the grain-filling period can explain more than 80 % of the yield variation and affect the yield by influencing the number of days required to reach the maximum grain-filling rate (Vmaxd) and the duration of the active grain-filling period (DAP). (3) The growing degree days (GDD) is the most significant controlling factor affecting yield; however, the effect of heat degree days (HDD) cannot be ignored. The HDD and cumulative thresholds of HDD in the EP and AP of grain-filling can better reflect the effect of heat on yield. The accumulation thresholds of HDD at Hebi and Suzhou were 28.1 °C·d and 15.2 °C·d in the EP period, and 31.0 °C·d and 14.9 °C·d in the AP period, respectively. The results provide a basis for the precise identification of heat disasters during grain-filling and the scientific adjustment of sowing dates.

Wing lengths of three Arctic butterfly species decrease as summers warm in Alaska

Climate warming can cause arthropods to express plastic and/or evolved changes in morphology. Previous studies have demonstrated that body sizes of Arctic butterflies are influenced by the temperatures experienced as larvae. To investigate whether this was occurring among Alaskan butterflies, we analyzed temporal trends in the wing sizes of three Holarctic species, Colias hecla, Boloria chariclea and Boloria freija, using museum specimens collected in Arctic tundra regions of Alaska between 1971 and 1995. Wing length was compared to accumulated growing degree days (GDD) during both the spring of the year collected and the previous year's summer during the normal period of larval development. We used mixed‐effects models to test if spring and summer temperatures affected adult morphology. Results show that for every 1°C increase in average seasonal temperature, wingspans decreased between 0.7 and 5 mm, with B. freija the most strongly affected. Our results suggest that the morphological sensitivity of Arctic butterflies to warming is the outcome of interactions between life‐history traits and regional climate, with all species sensitive to warming the summer before the flight year as well as warming the spring of the flight year. Boloria freija, which overwinters as late instar larvae that do not feed before pupation the following spring, was particularly strongly affected by summer warming.

The response of yield, number of clusters, and cluster weight to meteorological factors and irrigation practices in grapevines: A multi-experiment study

Knowledge of the yield components of wine grapevines is essential to achieve target production and effectively design vineyard management. Therefore, it is necessary to map and analyze the factors that control the yield components to optimize wine production and profitability. The objective of this study was to identify the ranked effects of environmental and agrotechnical factors (e.g., meteorological, physiological, vegetative, and irrigation) on grapevine yield, number of clusters, and cluster weight, and to quantify their patterns. To achieve this objective, a multi-experiment database was used, assembling data collected during four long-term irrigation experiments including different varieties and diverse climatic regions across Israel. First, a paired analysis of the yield components was conducted to define the relationships among them. Then, prediction models for the three variables (i.e., yield components) were designed using the support vector machine (SVM) algorithm to explore the ranked effects and patterns of the controlling factors. Validation was conducted using a test set, consisting of 20% of the dataset. The relationships between yield and cluster weight (0 < r < 0.73) and between yield and the number of clusters were always positive (0.67 < r < 0.77), while negative relationships were found between cluster weight and number of clusters (-0.61 < r < 0). The accuracy of the prediction models was high, with mean absolute errors (normalized to the range) ranging between 4.1%-8.11%. The most dominant factors affecting yield components were cumulative leaf area index, accumulated growing degree days (GDD), cumulative irrigation, and evapotranspiration. Leaf area showed an optimum point, below and above which the values of the yield components decrease; GDD negatively affected yield and cluster weight but had a positive effect on the number of clusters; irrigation was positively associated with yield components but leveled out above 348-395 mm per season. Reference evapotranspiration had a negative effect on yield and cluster weight, and a generally positive effect on the number of clusters. Utilizing data from multiple experiments and applying multivariate analysis, allow to define general patterns of relationships and interactions between controlling factors and yield components. Extracting this knowledge from the data and managing the growing amounts of information can be achieved through complex modeling and the implementation of standard protocols for data collection and monitoring.

Phenological, growth, and yield responses of sweet corn to elevational air temperatures in the humid tropics

Temperature plays a determinant role in the rate of plant metabolic activities and, accordingly, dictates the plant's phenological stages, growth, and productivity. The objective of this study was to evaluate the phenological, growth, and ear yield behavior of ten sweet corn hybrids in response to elevational temperature changes in the humid tropics. Seeds of ten hybrids were grown in three elevations (coastland, midland, and highland). Results show that the hybrids belonged to the same maturity group with comparable accumulated growing degree days, but they varied in green ear yield, harvest index, and heat use efficiency. It was also found that higher elevations lead to longer plant growth and fewer growing degree-day requirements, resulting in increased in biomass accumulation for ear and overall vegetative growth but more efficient in the heat use. These findings will be useful as a guide for crop management scheduling, including fertilizers application, pest control, diseases control, and weed control, to maximize sweet corn yield at different elevations in the humid tropics.

The impact of volcanism on Scandinavian climate and human societies during the Holocene: Insights into the Fimbulwinter eruptions (536/540 AD)

Recent paleoclimatic research has revealed that volcanic events around 536–540 AD caused severe, short-term global cooling. For this same period, archeological research from various regions evidences significant cultural transformation. However, there is still a lack of understanding of how human societies responded and adapted to extreme climate variability and new circumstances. This study focuses on the effects of the 536/540 AD volcanic event in four Scandinavian regions by exploring the shift in demographic and land use intensity before, during, and after this abrupt climate cooling. To achieve this, we performed climate simulations with and without volcanic eruptions using a dynamically downscaled climate model (iLOVECLIM) at a high resolution (0.25° or ~25 km). We integrated the findings with a comprehensive collection of radiocarbon dates from excavated archeological sites across various Scandinavian regions. Our Earth System Model simulates pronounced cooling (maximum ensemble mean −1.1°C), an abrupt reduction in precipitation, and a particularly acute drop in growing degree days (GDD0) after the volcanic event, which can be used to infer likely impacts on agricultural productivity. When compared to the archeological record, we see considerable regional diversity in the societal response to this sudden environmental event. As a result, this study provides a more comprehensive insight into the demographic chronology of Scandinavia and a deeper understanding of the land-use practices its societies depended on during the 536/540 AD event. Our results suggest that this abrupt climate anomaly amplified a social change already in progress.

Artificial Intelligence &amp; Nature-Based Solutions in Agriculture: A BT Cotton Pest Management Case Study in India

Artificial intelligence (AI)-based pest management advisory, based on integrated pest management (IPM), provided to cotton farmers on smartphones, resulted in a reduction in pest attacks &amp; up to 22% higher income in the 1 year 2020-21 in Ranebennur, Karnataka, and Wardha, Maharashtra states. However, no significant benefit was seen in a multi-state experiment in 2021-22 due to unusually high rainfall, resulting in lower pest attacks. The artificial intelligence was used in pest detection &amp; counting insect numbers in the pheromone trap to decide if threshold numbers were reached for pesticide spraying decisions. This was 1-2 weeks in advance of mass pest emergence and could control it to reduce crop damage. It required manual trap checking by the farmers on a weekly basis, which many farmers disliked. Artificial intelligence coupled to remote sensing, GIS, and/or farm sensors can benefit the farmers by cutting costs, increasing yield, and enabling cleaner production. Lower environmental pollution and less risk to farmers and consumers are co-benefits of the AI-IPM package. However, mating disruption technology, a competitor, includes putting 4-6 pheromone traps per acre for the mass capture of moths. It is organic-compatible, and another competitor is the mechanical growing degree day (GDD)-based IPM advisory, such as that provided by the startup “Fasal.” These are unintelligent, mechanical, but very effective algorithms. Thus, a cautious, logical, and gradual approach is needed in promoting AI in agriculture, also keeping in mind its impact on labour displacement.

Predicting Floral Bud Progression for Three Peach Cultivars

The prediction of floral bud progression in commercial peach cultivars promotes knowledge about the adaptability of cultivars to the climatic conditions in a specific location under a climatic variability context. Phenology is relevant to improving the scheduling of cultural practices in peach orchards. This research aimed to predict the floral bud progression of three peach cultivars: ‘Harvester’, ‘Red Globe’, and ‘Rubyprince’. Floral bud progression was assessed using one-year-old shoots collected from an orchard located at the Chilton Research and Extension Center, Alabama. Samples were evaluated under laboratory and growth chamber conditions. We recorded the flower developmental stages daily to identify the transition among stages. Daily temperature records were used to estimate the heat requirement in terms of Growing Degree Days (GDD). After dormancy release, samples needed between 23 to 39 and 37 to 42 days to reach the petal fall stage for season 1 and season 2, respectively. In terms of heat requirements, cultivars needed between 425.5 to 721.5 and 684.5 to 777 GDD to end flowering. A logistic curve was adjusted to describe the growth of the different floral stages over time. The results are key to supporting peach growers in crop management practices such as orchard establishment, irrigation, fertilization, freeze and frost protection, and pollination under climate variability scenarios.

Climate change impact on Spodoptera frugiperda (Lepidoptera: Noctuidae) life cycle in Mozambique

Although different seasonal cues are important for fall armyworm (FAW, Spodoptera frugiperda J.E. Smith) survival, it is known that the life cycle of this insect is strongly dependent on air temperature, means that its development rate proceeds faster when the weather is warm. To develops the insect needs to accumulate an amount of thermal units, as known as Growing Degree-Days (GDD). However, with the climate change driven by global warming, the GDD pattern must be changed and therefore, the life cycle of this new bug in Mozambique may be different from that observed in its native region. In the present study it is estimated the possible changes of FAW life cycle by applying the GDD method over Mozambique, under two representative scenarios of climate changes, RCP4.5 and RCP8.5 for 2070–2099 relative to present climate (1971–2000). For this purpose, dynamical downscaling process through the regional model RegCM4, nested to global model HadGEM2 were used. The outputs of air temperature dataset from the simulations were used to compute the accumulated GDD and hence the FAW number of generations (NG) during the summer-season over the study domain. The findings indicate that there is a bipolar pattern of GDD accumulation, being negative over most of central and restricted areas in southern region, and positive in northern region, altitude-modified climate areas over central region, and over southernmost areas for both representative climate scenarios, relative to present climate. Meanwhile, there is an increase (decrease) in NG in the areas of higher (lower) increase in air temperature for both future scenarios relative to present climate.

Alnus viridis: an encroaching species with valuable nutritional value reducing livestock greenhouse gas emissions

Alnus viridis is a shrub species that has been increasing in many European mountains over the past decades, leading to a loss of agricultural areas and several negative environmental impacts. Recently, targeted livestock management systems have been investigated to reduce its encroachment. This study aims to provide an exhaustive assessment of A. viridis leaf composition and its temporal variation across the grazing season. A. viridis leaves were collected throughout the summers of 2020 and 2021 in four encroached sites across the Swiss and Italian Alps, characterized by different pedo-climatic conditions. Based on the data collected by meteorological stations, the growing degree days (GDD) were calculated for each site and year. The leaf functional traits (i.e., Leaf Dry Matter Content – LDMC – and Specific Leaf Area – SLA), chemical composition (macro- and micro-elements and fibre content), phenolic content, in vitro organic matter digestibility (IVOMD), and related gas production (CO2 and CH4) were assessed. The LDMC significantly increased throughout the season, whereas the SLA decreased. All macro- and micro-elements significantly varied during the season, with leaf nitrogen (N) and phosphorus (P) decreasing. In contrast, leaf fibre contents significantly increased as the season advanced. There was a significant increase in condensed tannin content during the summer season. Finally, adding A. viridis leaves (20% of diet on a dry matter basis) to cattle diets significantly reduced IVOMD, methane produced per digested organic matter (CH4/dOM) and CO2/dOM, compared to a 100% hay diet. Moreover, CH4/dOM and CH4/CO2 ratio increased during the summer season. These results highlight the potential of A. viridis leaves as a valuable forage resource, especially at the beginning of the summer. Such information could be used to optimize grazing of robust livestock in A. viridis-encroached alpine pastures in order to reduce its invasion and minimize greenhouse gases production at the same time.

Nitrogen rate and harvesting time based on growing degree days influenced winter cereal rye morphological traits, forage yield, quality, and farm profit in poorly drained Alfisols

AbstractWinter cereal rye (Secale cereale L.) (WCR) is often double cropped with maize for silage (Zea mays L.) to increase farm forage supply and profit. Spring nitrogen (N) fertilization to WCR could influence its production and quality at different harvesting times. Therefore, two on‐farm trials were conducted in the 2019–2020 and 2020–2021 growing seasons to evaluate the effect of harvesting time (late‐March to end‐of‐April considering the growth stage) and spring N fertilization (0, 23, 47, and 71 kg N ha−1) on WCR morphology, forage yield, nutrient removal, quality, and farm profit. Only two N treatments (0 and 47 kg N ha−1) were evaluated for each harvesting time in trial 1. A quadratic model best explained an increase in WCR dry matter (DM) yield in response to growing degree days (GDD) accumulation (R2 = 0.81). An increase in GDD linearly decreased WCR relative forage quality (RFQ). Benchmarking RFQ at 150 for dairy milk production indicates WCR should be harvested at a GDD of 543, at which WCR plant height was 31.8 cm and DM yield was 0.77 Mg ha−1. This resulted in loss of profit in both study years compared to later harvesting dates at higher GDDs. Benchmarking RFQ at 125 for heifer production indicated that harvest should occur at a GDD of 668, at which the WCR was 71 cm tall, and its DM yield was 2.25 Mg ha−1. Nitrogen balances were negative at the N0 treatment (0 kg N ha−1), indicating a need for some N to maximize WCR yield. We found that a rate between 21 and 47 kg N ha−1 maximizes yields reflecting slightly positive balances, in which the highest profits occur. Our results suggest that the harvesting time can be predicted by GDD and should be adjusted for RFQ. We conclude that less than 47 kg N ha−1 N fertilizer is required for WCR production in soils with manure history and high soil organic matter (&gt;30 g kg−1).

Learning to balance wheat G × E × M interactions in response to a changing climate—The case for ultra‐early planting systems

AbstractReported global reductions in cereal grain yields due to increased global average temperature combined with increasing global populations peaking near 2050 create an immediate need to increase cereal grain yield potential and reduce the yield gap between realized on‐farm grain yield and potential yield. The development of an ultra‐early planting system for spring wheat (Triticum aestivum L.) on the northern Great Plains can increase the resiliency of current growing systems to a changing climate. This was achieved through the development of a unique set of practices designed to successfully shift current wheat production systems to ultra‐early growing systems. Ultra‐early‐planted wheat‐growing systems on the northern Great Plains will provide immediate benefits to the adopting producer in the form of increased grain yield and increased grain yield stability relative to current practices. As global average temperatures warm, and atmospheric CO2 concentrations increase, the northern Great Plains region is in a unique position to potentially realize grain yield increases rather than temperature‐driven grain yield decreases. Shifting planting earlier and taking advantage of increased growing degree day accumulation and water use efficiencies while avoiding higher temperatures during sensitive physiological periods are tactics implemented in ultra‐early growing systems that will increase in importance and relevance in the next three decades as average daily temperatures increase.