Entire Growth Season Research Articles

Assessing climate effects on wheat yield heterogeneity in the North China Plain and evolution from 1960 to 2020

This study quantified climate effects on wheat yield heterogeneity in the North China Plain from 1960 to 2020, by integrating the Agricultural Production Systems sIMulator, Optimal Parameters–based Geographical Detector model, and Ensemble Empirical Mode Decomposition model. The factors dominating yield heterogeneity varied by growth stage. For sowing to anthesis, anthesis to maturation, and the entire growth season, minimum temperature, radiation, and vapor pressure deficit has the greatest effect on yield heterogeneity. Interannual periodic oscillations govern the long–term evolution of climate effects on yield heterogeneity from 1960 to 2020.

Drying out fish ponds, for an entire growth season, as an agroecological practice: maintaining primary producers for fish production and biodiversity conservation.

Agroecology largely focusses on terrestrial agroecosystems, but it can also be applied to fish farming. Indeed, ponds are typically used for fish production in Europe, but are also important reservoirs of biodiversity. Numerous studies demonstrate that both fish production and biodiversity are strongly determined by human management. One key practice in extensive fish farming, although more rare in Europe, is to dry out ponds. They are left dry for a complete year after several years of fish production. However, the extent to which this practice affects the functioning of the ecosystem, its biodiversity and fish production remain unclear. We investigated data from 85 fish ponds in the Dombes region, France, sampled between 2007 and 2014. We related variation in key abiotic characteristics to the time since last dry out. The dataset included organic matter content in pond sediments and concentrations of inorganic nitrogen and phosphorus in the water column, and biotic components such as macrophytes cover and richness, phytoplankton concentration and richness, macroinvertebrates density, and fish yield. Our results show that drying out facilitates the mineralization of organic matter in sediments and results in higher concentrations of inorganic nitrogen in the water column. Macrophytes cover is highest during the first year after drying out, and gradually declines after at the expense of increasing phytoplankton concentration. The diversity of both is highest in the first year after drying out and declines rapidly, especially for macrophytes. Fish yield is at its maximum in the second year. Drying out fish ponds appears to be an important agroecological practice in extensive fish farming with an application every three to four years. By nutrient recycling, this practice has a positive impact on the balance between primary producers and indirectly on the whole food web during two years. It optimizes fish production and allows biodiversity conservation.

Optimizing radiation capture in machine-harvested cotton: A functional-structural plant modelling approach to chemical vs. manual topping strategies

Topping and planting density are key agronomic management practices to optimize cotton plant structure for machine harvesting and light capture. However, modelling the effects of these practices on canopy light utilization in the field, in order to improve cotton management, remains challenging. Functional-structural plant modelling is a computational approach to explore the effects of agronomic practices on shaping plant architecture and thus light interception. This study, for the first time, utilizes the CottonXL model to quantify the significant impact of chemical topping compared to manual topping on radiation interception on machine-harvested cotton in China at different planting densities, providing new management strategies for cotton production. A more compact plant structure is shaped by chemical topping through inhibiting leaf expansion and shortening internodes of both the main stem and fruiting branches, thereby allowing more PAR interception by middle and lower leaves. Simulation results showed that the total PAR intercepted by the canopy over entire growth season was increased by 11.3 % under chemical topping compared to manual topping. This positive effect became even more pronounced with increasing plant density. These results indicate that chemical topping could be beneficial for optimizing canopy structure, enhancing light interception and lint yield, especially at higher plant densities. The results illustrate the importance of shaping plant structure for improving radiation resource capture and exemplify the potential of optimizing topping strategy and plant density to enhance crop performance. They also demonstrate the utility of a functional-structural plant model for guiding crop management.

A Comparison of Greenhouse Gas Emission Patterns in Different Water Levels in Peatlands

Peatlands store large amounts of carbon in wetland ecosystems. The hydrological conditions within peatlands are important factors that affect the biochemical cycle and patterns of greenhouse gas emissions in these peatlands. This study was carried out in Changbai Mountain Jinchuan peatland to investigate variations in carbon dioxide and methane emissions in peat swamps that have undergone distinct saturation conditions. Three peatland types (high water levels (S1); medium water levels (S2); low water levels (S3)) at different flood depths were selected as specific sampling points. The static box and gas chromatography methods were used at different time periods (6:00; 12:00; and 18:00) from July to September. The discharge flux of CO2 and CH4 slowly increased with the increase in the water level. The results indicate similarity in the fluctuation trends between the fluxes of CO2 and CH4 in S1 and S2 to the fluctuations of water levels. During the entire growth season, the flux range of CO2 and CH4 was −695.329~859.907 mg m2h−1 and 259.981~147.155 mg m2h−1, respectively. Furthermore, there was variation in mutation characteristics between two gases, the CO2 exhibited larger mutation range (−7.08~3.40) than CH4 (−1.79~1.26). In terms of daily flux changes, CO2 showed an upward trend, while CH4 had a downward trend. These results indicate variations in saturation conditions tend to affect discharge of greenhouse gases, with subsequent effects on climate change. This study highlights potential theoretical support to reduce anthropogenic activities on peatlands. This can be achieved by undertaking measures to conserve peatlands and explore mitigation measures to minimize greenhouse gas emissions and hence impacts of climate change.

Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery.

Aboveground biomass (AGB) is a crucial physiological parameter for monitoring crop growth, assessing nutrient status, and predicting yield. Texture features (TFs) derived from remote sensing images have been proven to be crucial for estimating crops AGB, which can effectively address the issue of low accuracy in AGB estimation solely based on spectral information. TFs exhibit sensitivity to the size of the moving window and directional parameters, resulting in a substantial impact on AGB estimation. However, few studies systematically assessed the effects of moving window and directional parameters for TFs extraction on rice AGB estimation. To this end, this study used Unmanned aerial vehicles (UAVs) to acquire multispectral imagery during crucial growth stages of rice and evaluated the performance of TFs derived with different grey level co-occurrence matrix (GLCM) parameters by random forest (RF) regression model. Meanwhile, we analyzed the importance of TFs under the optimal parameter settings. The results indicated that: (1) the appropriate window size for extracting TFs varies with the growth stages of rice plant, wherein a small-scale window demonstrates advantages during the early growth stages, while the opposite holds during the later growth stages; (2) TFs derived from 45° direction represent the optimal choice for estimating rice AGB. During the four crucial growth stages, this selection improved performance in AGB estimation with R2 = 0.76 to 0.83 and rRMSE = 13.62% to 21.33%. Furthermore, the estimation accuracy for the entire growth season is R2 =0.84 and rRMSE =21.07%. However, there is no consensus regarding the selection of the worst TFs computation direction; (3) Correlation (Cor), Mean, and Homogeneity (Hom) from the first principal component image reflecting internal information of rice plant and Contrast (Con), Dissimilarity (Dis), and Second Moment (SM) from the second principal component image expressing edge texture are more important to estimate rice AGB among the whole growth stages; and (4) Considering the optimal parameters, the accuracy of texture-based AGB estimation slightly outperforms the estimation accuracy based on spectral reflectance alone. In summary, the present study can help researchers confident use of GLCM-based TFs to enhance the estimation accuracy of physiological and biochemical parameters of crops.

Hydrogen isotope biogeochemistry of plant waxes in paired lake catchments

Studies of plants in modern catchment systems can serve to improve the level of understanding of sedimentary plant wax hydrogen isotope (δ2H) data by directly investigating the link between local climate conditions, plant source water, leaf water, and plant lipids for individual plant species. Here we present such an application to compare two lake catchments with different basin morphologies in Estonia. We sampled leaf and xylem water, as well as leaf waxes from the seven common plant species in each catchment, and soil water. We then measured the δ2H values of all waters, and of n-alkanes (δ2Hn–alk) from the plant waxes, as well as surface lake sediments. We applied a Péclet modified Craig-Gordon leaf water model using local precipitation isotope and climate data to characterize the δ2H values of the biosynthetic source water pool throughout the entire growth season. The data and model results provide a detailed view of how the input hydro-climatic signals from the precipitation δ2H values were modified by environmental and plant physiological conditions and ultimately by the biosynthetic isotope fractionation associated with n–alkane production by each plant species. We report both average apparent (εapp = –92 ± 21 ‰) and biosynthetic (εbio = –132 ± 19 ‰) hydrogen isotope fractionation factors of all species from the studied catchments. This information serves as a foundation for sedimentary organic geochemistry and paleoclimate studies, which allows for more direct and quantitative links to be made between sedimentary plant wax δ2H values and the climate signal contained in plant source water.

Drying out fish ponds, for an entire growth season, as an agroecological practice: maintaining primary producers for fish production and biodiversity conservation

Agroecology largely focusses on terrestrial agroecosystems, but it can also be applied to fish farming. Indeed, ponds are typically used for fish production in Europe, but are also important reservoirs of biodiversity. Numerous studies demonstrate that both fish production and biodiversity are strongly determined by human management. One key practice in extensive fish farming, although more rare in Europe, is to dry out ponds. They are left dry for a complete year after several years of fish production. However, the extent to which this practice affects the functioning of the ecosystem, its biodiversity and fish production remain unclear. We investigated data from 85 fish ponds in the Dombes region, France, sampled between 2007 and 2014. We related variation in key abiotic characteristics to the time since last dry out. The dataset included organic matter content in pond sediments and concentrations of inorganic nitrogen and phosphorus in the water column, and biotic components such as macrophytes cover and richness, phytoplankton concentration and richness, macroinvertebrates density, and fish yield. Our results show that drying out facilitates the mineralization of organic matter in sediments and results in higher concentrations of inorganic nitrogen in the water column. Macrophytes cover is highest during the first year after drying out, and gradually declines after at the expense of increasing phytoplankton concentration. The diversity of both is highest in the first year after drying out and declines rapidly, especially for macrophytes. Fish yield is at its maximum in the second year. Drying out fish ponds appears to be an important agroecological practice in extensive fish farming with an application every three to four years. By nutrient recycling, this practice has a positive impact on the balance between primary producers and indirectly on the whole food web during two years. It optimizes fish production and allows biodiversity conservation.

Climate Variations in the Low-Latitude Plateau Contribute to Different Sugarcane (Saccharum spp.) Yields and Sugar Contents in China.

In China, the main sugarcane (Saccharum spp.) planting areas can be found in the low-latitude plateau (21° N-25° N, 97° E-106° E), which has most of the natural ecological types. However, there is limited information on the climate conditions of this region and their influence on sugarcane yield and sucrose content. Monthly variations in the main climate factors, namely, average air temperature (AAT), average relative humidity (ARH), average rainfall amount (ARA), and average sunshine duration (ASD), from 2000 to 2019 and sugarcane yield and sucrose content of 26 major sugarcane-producing areas from 2001/2002 to 2018/2019 were collected from the low-latitude plateau in Yunnan for studying the impact of climate variations on sugarcane yield and sucrose content. The results showed that AAT in the mid-growth season had a significant positive correlation with sucrose content (p < 0.05), and AAT in the late-growth season had a very significant positive correlation with sucrose content (p < 0.01). ARH in the mid-growth season had a significant positive correlation with sugarcane yield (p < 0.05). ARA in the early-growth season showed a significant positive correlation with sugarcane yield (p < 0.05). ASD in the late-growth season had a significant positive correlation with sugarcane yield (p < 0.05) and sucrose content (p < 0.01). The rainy and humid sugarcane areas were characterized by high ARA and ARH during the entire growth period, low AAT and ASD in the mid-growth season, and low AAT in the late-growth season, contributing to a high sugarcane yield, but not a high sucrose content. The low temperature and sunshine semi-humid sugarcane areas were characterized by the lowest AAT in the early and middle stages of sugarcane growth, less ASD in the early and middle stages, and less ARA in the early and late stages, which are unfavorable for sugarcane yield and sucrose content. The high temperature and humidity sugarcane areas were characterized by higher AAT and ARA, and moderate ASD during the entire growth period, resulting in good sugarcane growth potential and contributing to the sugarcane yield and sucrose content. The semi-humid and multi-sunshine sugarcane areas were characterized by the lowest ARH in the entire growth period, the lowest ARA in the middle and late seasons, and the longest ASD, contributing to an increase in sucrose content. The humid and sunny areas were characterized by the longest ASD and high ARH in the early and late seasons of sugarcane growth and moderate AAT and ARA during the entire growth season, which are beneficial for high sugarcane yield and sucrose content. Overall, these findings suggest that the sugarcane variety layout should be based on the climate type (of which there are five in the plateau), and corresponding cultivation practices should be used to compensate for the climatic conditions in various growth stages.

Ensemble surface soil moisture estimates at farm-scale combining satellite-based optical-thermal-microwave remote sensing observations

Remote sensing presents great potential for soil moisture mapping, although a lack of high-resolution, all-season suitable data and model-based complications hinders continuous mapping at the farm scale. The current study attempts to access optical (Sentinel-2) and microwave (Sentinel-1) based Water Cloud Model (WCM), optical and/or thermal-based Thermal-Optical TRApezoid Model (TOTRAM), and Optical TRApezoid Model (OPTRAM) utilizing Landsat-8 data, and an ensemble of these three models synergistically using microwave, optical and thermal remote sensing data for fourteen dates spanning two post-monsoon periods over a semi-arid irrigated agricultural farm. The ensemble model developed with Gradient Boosting Machine (GBM) offered a superior SSM estimate for the entire crop growth season. The ensemble model had the lowest RMSE (0.053 m3 m−3), followed by OPTRAM (SWIR2) (0.06 m3 m−3), while TOTRAM recorded the highest RMSE (0.092 m3 m−3), even though the model performances varied throughout crop growth cycles. For SSM mapping in the early phases of crop growth, WCM or TOTRAM were found superior, while during the latter growth stages, the performance of OPTRAM was better. In contrast to TOTRAM, which needs local calibration as the land surface temperature is sensitive to atmospheric conditions, the study emphasizes the shortcomings of WCM in terms of calibration requirements for changing vegetation structure utilizing in situ data. OPTRAM is suitable for generating SSM maps in semi-arid environments with a predominance of clear skies throughout the post-monsoon season. It is straightforward, low data, and resource intensive with universal surface reflectance-soil moisture association. The ensemble technique can be recommended for farm-level irrigation mapping over a post-monsoon period.

Evaluating the effects of plastic film mulching duration on soil nitrogen dynamic and comprehensive benefit for corn (Zea mays L.) field

Regulating the plastic film mulching duration was a good strategy to decrease plastic residues and to avoid the stress from low soil nitrate concentration (SNC). However, few studies focused on soil nitrogen dynamic under different mulching durations, especially for the impacts of soil nitrogen loss on crop yield and comprehensive benefits have not been revealed. In this study, HYDRUS (2D/3D) model was used to evaluate the differences of SNC and NO3-N leaching (NL) under no mulching (NM), mulching for the seedling stage (PMS), mulching for the elongation stage (PME), mulching for the tasseling stage (PMT), and mulching for the entire corn growth season (PMW). Additionally, a two-year (2019–2020) field experiment was carried out to measure SNC, NL, N2O, and NH3 emissions and to calibrate and validate the model accuracy. The effect of soil nitrogen loss on the comprehensive benefits under different mulching durations was further analyzed using a comprehensive multi-objective model. The results showed that HYDRUS (2D/3D) could well capture SNC dynamic under different mulching treatments, with average the normalized root mean square error of 16.5% in the validation period. There was no apparent difference in SNC, N2O emission, NH3 emission, and NL under different mulching durations in the seedling stage. However, the corresponding difference markedly increased with corn growth and development, especially in 2019. The average SNC (0–20 cm soil depth) under NM was higher by 9.2%, 25.0%, 21.2%, and 19.4% than PMS, PME, PMT, and PMW, respectively. Nitrogen emission (N2O and NH3) showed an increase trend with the shortening of mulching duration, i.e., NM>PMS>PME >PMT >PMW, while an opposite trend was found for NL. In general, PME was found to be the optimal mulching duration with the highest comprehensive evaluation score of 1782.7. Therefore, PME can be recommended to apply in this region to promote the developments of sustainable agriculture.

Phytoplankton dynamics in nearshore regions of the western Antarctic Peninsula in relation to a variable frontal zone in the Gerlache Strait

The Gerlache Strait is a narrow channel that separates the western coast of the Antarctic Peninsula (WAP) from the Palmer Archipelago. This area is characterized by the presence of interconnected fjords, bays, islands, and channels that serve as a refuge for megafauna during summer. Through the framework of FjordPhyto – a citizen science collaboration with the International Association of Antarctica Tour Operators (IAATO) vessels – we assessed phytoplankton biomass and composition in surface waters of six under-explored nearshore areas connected to the Gerlache Strait (between 64° and 65° S) during three consecutive seasons, from November to March (2016–2019). During the first two seasons, we found significant differences in the phytoplankton community distribution and successional patterns to the north and south of the sampling area; the greatest differences were evidenced mainly in the months of high biomass, December and January. During December, cryptophytes bloomed in the north, while microplanktonic diatoms dominated in the south, and during January, small centric diatoms dominated in the north, while prasinophytes bloomed in the south. This spatial distinction in phytoplankton communities were mainly associated with the occurrence of a surface thermal front in the Gerlache Strait around 64.5° S. The presence of the front separating warm waters to the north and colder waters to the south, during the months of December to February, was confirmed by the analysis of 10 years of remote sensing data. By contrast, during the third season, low biomass prevailed, and no differences in the phytoplankton composition between the north and south areas were observed. The third season was the coldest of the series, with smaller differences in water temperature north and south of the usual front location. This study shows for the first time a complete overview of the phytoplankton composition throughout the entire growth season (November through March) in the nearshore areas of the WAP between 64° and 65° S. The results of this work contribute to the understanding of the phytoplankton community in relation to small scale physical features during the Antarctic austral summer.

The results of studying the productivity indicators of three-line sunflower hybrids and their parental forms under the influence of weather conditions

Topicality. At the present stage, the basis of the Ukrainian economy is agricultural production. Agriculture is the only sphere that is as dependent on weather fluctuations as any other. And it constantly has to adapt to the ongoing climate change. Climate changes, which cause an increase in temperatures, changes in the amount of precipitation and its distribution during the growth season, unstable weather conditions, the spread of pests and diseases, threaten the production of sunflower hybrids. The question about the influence of weather factors on the formation of productive traits of both hybrids and their parental forms, the dependence of agricultural crops on weather factors (temperature and precipitation, both for the entire growth season and for several months) is relevant. Purpose. To study of the influence of weather conditions on the formation of productive traits of hybrids and their parental components. Materials and Methods. The triple hybrids of sunflower (Helianthus annuus L.) Ahent, Ahronomicnyi, Kameniar, Marshal, Zaporizkyi 28, their parental components – single-cross sterile hybrids ZL(22х102)A, ZL(42х46)A, ZL(42х58)A and the restorers of fertility ZL512Rf, ZL678Rf and ZL7034Rf were studied. Results. Over the years of research, yield fluctuations reached to 0.90 t/ha for Ahent hybrid, 1.66 t/ha – Agronomichnyi, 0.13 t/ha – Marshal, 0.96 t/ha – Kameniar and 0.58 t/ha – Zaporizkyi 28. The highest yields and oil yield per unit area were in 2018. According to the results of three-year trials on hybrids and their parental forms, the Marshal hybrid had the highest yield – 2.64 t/ha with seed oil content of 50.78 % in 2016. Also in 2016, all pollen fertility restorers had the highest yields and oil yields. In 2018, four out of five hybrids showed the highest yields and oil yield per unit area. In 2017, the lowest oil content in the seeds of the tested hybrids was in the range of 45.16–48.49 % for Ahent and Zaporizkyi 28, respectively. Conclusions. It was established that Marshal is the most productive hybrid for sunflower cultivation in the southern Steppe of Ukraine, its average yield was 2.56 t/ha that exceeded by 0.27–0.48 t/ha this indicator in other hybrids. Key words: sunflower, hybrid, yield, oil content, 1000 seeds weight, oil yield, weather conditions, precipitation, temperature, sum of active temperatures, correlation coefficient

Quantifying interspecies competition for water in tomato–corn intercropping system using an improved evapotranspiration model considering radiation interception by neighboring plants in two-dimensional profile

Intercropping system is widely adopted in agricultural production due its significant advantages. Usually, the radiation interception of plants with low canopy would be decreased in the vertical and horizontal directions because of the heterogeneity of the underlying surface of the intercropping system, which causes reduction in the water consumption of such plants. However, the existing evapotranspiration (ET) models consider the effect of radiation interception on water consumption only in the vertical direction.Thus, the objectives of this study were to quantify water competition between intercropping components in the tomato-corn intercropping system with various spatial arrangements and to determine suitable spatial arrangements. An improved ET model (DERIN) was deducted by inducing a two-dimensional radiation interception model based on ERIN model, and the corresponding accuracy of ET estimation was compared with that of ERIN and MERIN models. Additionally, a 2-year (2018–2019) field experiment was conducted to validate the accuracy of the model and assess interspecies competition for water in the following systems: two rows of tomato intercropping with two rows of corn (IC2–2), four rows of tomato intercropping with two rows of corn (IC4–2), sole corn (SC), and sole tomato (ST). The result showed that the DERIN modelcan precisely quantify the radiation interception by crop canopy in various intercropping systems compared with ERIN and MERIN models. Mean relative error (MRE) of ET estimated by DERIN model in various intercropping systems (i.e., IC2–2 and IC4–2) decreased by 59.9% compared with that estimated by MERIN model. Moreover, the average MRE of evaporation estimated by DERIN model decreased by 53.8% and 79.8% compared with MERIN and ERIN models, respectively. The interspecies competition for water between tomato and corn in various planting systems was similar during the entire crop growth season. The highest water competition ratio for corn (CRc) occurred in IC2–2 system among various intercropping systems, whereas IC4–2 system exhibited the highest land equivalent ratio for water (LERT). Average LERT of IC4–2 system increased by 6.7% and 5.8% compared with IC2–2 system in 2018 and 2019, respectively. Therefore, IC4–2 system can be recommended as the optimal intercropping system for sustainable agriculture development. Collectively, the DERIN model is an important tool to quantify interspecies water competition between commercial and grain crops. Also, the DERIN model is currently available method to optimize spatial arrangements of an intercropping system in different spatial position and planting orientation.

Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery.

Rapid and accurate assessment of yield and nitrogen use efficiency (NUE) is essential for growth monitoring, efficient utilization of fertilizer and precision management. This study explored the potential of a consumer-grade DJI Phantom 4 Multispectral (P4M) camera for yield or NUE assessment in winter wheat by using the universal vegetation indices independent of growth period. Three vegetation indices having a strong correlation with yield or NUE during the entire growth season were determined through Pearson's correlational analysis, while multiple linear regression (MLR), stepwise MLR (SMLR), and partial least-squares regression (PLSR) methods based on the aforementioned vegetation indices were adopted during different growth periods. The cumulative results showed that the reciprocal ratio vegetation index (repRVI) had a high potential for yield assessment throughout the growing season, and the late grain-filling stage was deemed as the optimal single stage with R2, root mean square error (RMSE), and mean absolute error (MAE) of 0.85, 793.96 kg/ha, and 656.31 kg/ha, respectively. MERIS terrestrial chlorophyll index (MTCI) performed better in the vegetative period and provided the best prediction results for the N partial factor productivity (NPFP) at the jointing stage, with R2, RMSE, and MAE of 0.65, 10.53kg yield/kg N, and 8.90kg yield/kg N, respectively. At the same time, the modified normalized difference blue index (mNDblue) was more accurate during the reproductive period, providing the best accuracy for agronomical NUE (aNUE) assessment at the late grain-filling stage, with R2, RMSE, and MAE of 0.61, 7.48kg yield/kg N, and 6.05kg yield/kg N, respectively. Furthermore, the findings indicated that model accuracy cannot be improved by increasing the number of input features. Overall, these results indicate that the consumer-grade P4M camera is suitable for early and efficient monitoring of important crop traits, providing a cost-effective choice for the development of the precision agricultural system.

A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval

Abstract. As changes to Earth's polar climate accelerate, the need for robust, long–term sea ice thickness observation datasets for monitoring those changes and for verification of global climate models is clear. By linking an algorithm for retrieving snow–ice interface temperature from passive microwave satellite data to a thermodynamic sea ice energy balance relation known as Stefan's law, we have developed a retrieval method for estimating thermodynamic sea ice thickness growth from space: Stefan's Law Integrated Conducted Energy (SLICE). With an initial condition at the beginning of the sea ice growth season, the method can model basin-wide absolute sea ice thickness by combining the one-dimensional SLICE retrieval with an ice motion dataset. The advantages of the SLICE retrieval method include daily basin-wide coverage, lack of atmospheric reanalysis product input requirement, and a potential for use beginning in 1987. Validation of the retrieval against measurements from 10 ice mass balance buoys shows a mean correlation of 0.89 and a mean bias of 0.06 m over the course of an entire sea ice growth season. Despite its simplifications and assumptions relative to models like the Pan-Arctic Ice–Ocean Modeling and Assimilation System (PIOMAS), basin-wide SLICE performs nearly as well as PIOMAS when compared against CryoSat-2 and Operation IceBridge using a linear correlation between collocated points.

Rapid Diagnosis of Nitrogen Nutrition Status in Summer Maize over Its Life Cycle by a Multi-Index Synergy Model Using Ground Hyperspectral and UAV Multispectral Sensor Data

Global climate change and the spread of COVID-19 have caused widespread concerns about food security. The development of smart agriculture could contribute to food security; moreover, the targeted and accurate management of crop nitrogen is a topic of concern in the field of smart agriculture. Unmanned aerial vehicle (UAV) spectroscopy has demonstrated versatility in the rapid and non-destructive estimation of nitrogen in summer maize. Previous studies focused on the entire growth season or early stages of summer maize; however, systematic studies on the diagnosis of nitrogen that consider the entire life cycle are few. This study aimed to: (1) construct a practical diagnostic model of the nitrogen life cycle of summer maize based on ground hyperspectral data and UAV multispectral sensor data and (2) evaluate this model and express a change in the trend of nitrogen nutrient status at a spatiotemporal scale. Here, a comprehensive data set consisting of a time series of crop biomass, nitrogen concentration, hyperspectral reflectance, and UAV multispectral reflectance from field experiments conducted during the growing seasons of 2017–2019 with summer maize cultivars grown under five different nitrogen fertilization levels in Beijing, China, were considered. The results demonstrated that the entire life cycle of summer maize was divided into four stages, viz., V6 (mean leaf area index (LAI) = 0.67), V10 (mean LAI = 1.94), V12 (mean LAI = 3.61), and VT-R6 (mean LAI = 3.94), respectively; moreover, the multi-index synergy model demonstrated high accuracy and good stability. The best spectral indexes of these four stages were GBNDVI, TCARI, NRI, and MSAVI2, respectively. The thresholds of the spectral index of nitrogen sufficiency in the V6, V10, V12, VT, R1, R2, and R3–R6 stages were 0.83–0.44, −0.22 to −5.23, 0.42–0.35, 0.69–0.87, 0.60–0.75, 0.49–0.61, and 0.42–0.53, respectively. The simulated nitrogen concentration at the various growth stages of summer maize was consistent with the actual spatial distribution.

Monitoring vineyard water status using Sentinel-2 images: qualitative survey on five wine estates in the south of France

The wine industry must face many challenges because of climate change. One of them is the increase in temperatures and droughts events. These changes sometimes lead to yield losses and can also impact the quality of the wine produced (e.g., increased alcohol content). The management of available water is also a sensitive issue as water requirements for vineyard irrigation are quickly increasing in the south of France. In this context, there is a need for a decision tool that can help evaluate the vine water status through the entire growth season at a large scale. To address this issue, we have previously developed a model (see Laroche-Pinel et al. 2021a) which predicts the vine Stem Water Potential (Ψstem) using Sentinel-2 (S2) images. This model was developed based on a field campaign over three years. The present study now aims to investigate the feedback of winegrowers on the outputs of our model. Therefore, it was applied on the plots of five wine estates that do not belong to the set used in the initial paper. The qualitative results show interesting spatial and temporal consistency in accordance with winegrower knowledge, irrigation data, and weather forecast. The predicted Ψstemhighlights spatial variability in vine fields where a water source emerges and reflects the differences between vine fields with a drip or sparkling irrigation or without an irrigation system. The predicted Ψstem also clearly reacts to a peak in temperature. According to their feedback, three of the five winegrowers would be glad to use this service in the years to come.

Modeling evapotranspiration and evaporation in corn/tomato intercropping ecosystem using a modified ERIN model considering plastic film mulching

Intercropping planting pattern under plastic film mulching (PFM) has been widely adopted in the arid regions to reduce soil evaporation (E), improve land-use efficiency, and increase crop yield. However, water competition between intercropping components in the soil-plant-atmosphere continuum remains largely unexplored. The evaporation and radiation interception using the neighboring species model (ERIN) can effectively estimate evapotranspiration (ET) in a different intercropping ecosystem. However, the effects of soil surface resistance in the mulching area on ET are not considered in the ERIN model. Thus, the existing ET models do not accurately estimate ET in the intercropping ecosystem with PFM. In this study, we proposed a modified ERIN model (MERIN). In the MERIN model, soil surface resistance in the mulching area was taken into account, and its performance was compared to ERIN and Penman-Monteith (PM) models. These models were validated against observed ET and E using the water balance method and micro-lysimeters in a corn intercropped tomato experiment under high (HI: 30 mm for corn and 22.5 mm for tomato, a locally recommended irrigation depth), medium (MI: 22.5 mm for corn and 16.9 mm for tomato, 25% of HI), and low irrigation depth (LI: 15 mm for corn and 11.25 mm for tomato, 50% of HI) during 2018-2019, respectively. The outcomes of this study showed that the MERIN model could accurately estimate ET and E variation for a corn-tomato intercropping ecosystem under PFM during the entire crop growth season compared to the other examined models. The most intense water competition between corn and tomato was observed in stage II (the elongation and tasseling stages for corn; the flowering and fruiting stages for tomato). T of corn generally was higher than tomato, but an opposite result was also observed in stage II. Additionally, the variation of water competition under different irrigation levels was similar in the intercropping ecosystem. When irrigation depth decreased to 22.5 and 15 mm from 30 mm, average T for corn decreased by 10.7% and 16.3%, respectively, and by 12.9% and 22.4% for tomato, respectively, in both years.

Determination of irrigation scheduling thresholds based on HYDRUS-1D simulations of field capacity for multilayered agronomic soils in Alabama, USA

The use of soil matric potential (ψ) sensors to support irrigation decisions has become common practice among many producers. However, ψ values at which irrigation should be initiated (ψlim) based on a pre-defined irrigation depth is still lacking. The main objectives of this study were: (i) evaluate the impact of different negligible drainage flux on estimated ψ at field capacity (ψfc) using HYDRUS 1D simulations; (ii) identify ψlim values and its corresponded irrigation depth under different soil profile depth at representative soil types in Northwest and Southeast Alabama. The ψ-θ relation at field capacity (ψfc and θfc, respectively) were estimated by a numerical internal drainage flux experiment for multilayered soils using HYDRUS-1D software simulations. Among the different negligible drainage fluxes (qfc) tested, a qfc value of 0.01 and 0.025 cm d−1 yielded the best results for the soil located at Northwest and Southeast Alabama, respectively. For a soil water depletion of 35% and a soil profile depth of 0.6 m, the ψlim ranged from − 103 to − 133 kPa for the soils located at Northwest Alabama and − 38 to − 51 kPa for soils located in Southeast Alabama. It returned an irrigation depth varying from 20 to 24 mm for Northwest Alabama soils and 15–33 mm for Southeast Alabama soils. For a same irrigation depth, it was observed that the ψlim increased (became less negative) as soil profile depth considered for irrigation calculations increased. Additionally, if the same pre-defined irrigation depth is used during the entire growing season, there is a high change that plants could be under stress due to the high level of water deficit. Using the same irrigation depth during the entire crop growth season could be a flawed irrigation management strategy; therefore, irrigation depth should dynamically change over the growth season as the plant roots reach deeper soil layers.

Cold and wet: Diatoms dominate the phytoplankton community during a year of anomalous weather in a Great Lakes estuary

As sentinels of climate change and other anthropogenic forces, freshwater lakes are experiencing ecosystem disruptions at every level of the food web, beginning with the phytoplankton, a highly responsive group of organisms. Most studies regarding the effects of climate change on phytoplankton focus on a potential scenario in which temperatures continuously increase and droughts intersperse heavy precipitation events. Like much of the conterminous United States in 2019, the Muskegon River watershed (Michigan, USA) experienced record-breaking rainfall accompanied by unusually cool temperatures, affording an opportunity to explore how an alternate potential climate scenario may affect phytoplankton. We conducted biweekly sampling of environmental variables and phytoplankton in Muskegon Lake, a Great Lakes Area of Concern that connects to Lake Michigan. We compared environmental variables in 2019 to the previous eight years using long-term data from the Muskegon Lake Observatory buoy, and annual monitoring excursions provided historical phytoplankton data. Under cold and wet conditions, diatoms were the single dominant division throughout the entire growth season – an unprecedented scenario in Muskegon Lake. In 10 of the 13 biweekly sampling days in 2019, diatoms comprised over 75% of the phytoplankton community in the lake by count, indicating that the spring diatom bloom persisted through the fall. Additionally, phytoplankton seasonal succession and abundance patterns typically seen in this lake were absent. In a world experiencing reduced predictability, increased variability, and regional climate anomalies, studying periods of extreme weather events may offer insight into how natural systems will be affected and respond under future climate scenarios.