Field Experimental Data Research Articles

Agrometeorological characteristics of spring barley in the Northern Trans-Urals

The purpose of this work was to study the effect of air temperature on the duration of the growing season in Western Siberia (Russia). Material and methods: phenological observation of 6 varieties of spring barley (Abalak, Zolotnik Naran, Tandem, DLA-14 and Pasadena) was carried out for the period from 2016 to 2020. The sensitivity of varieties to temperature conditions is determined. The optimal average daily air temperature during the interphase periods was established: shoots-heading 31-35 days, and heading-ripeness 34-37 days. The optimal sums of effective temperatures above 10 ºC, necessary for the growth, and development of spring barley varieties, have been calculated. The Abalak variety was distinguished by a narrow temperature range in both interphase periods (the first period - 0.7 ºC; the second - 2.3 ºC), which indicates a low plasticity of this variety. The agrometeorological characteristic obtained from the data of field experiments can be used to identify the areas of cultivation of varieties, as well as for a scientifically based assessment of the territory in relation to the variety. The optimal, minimum air temperatures for each variety are calculated. The sensitivity of varieties to temperature changes was studied.

Comparisons of apple cultivar suitability for Rhagoletis pomonella (Diptera: Tephritidae) from Washington State, United States of America

Abstract It is unclear whether larval infestations of Rhagoletis pomonella (Walsh) (Diptera: Tephritidae) in earlier (softer) apple (Rosaceae) cultivars are greater than in later (firmer) apple cultivars in Washington State, United States of America, where flies were introduced and are quarantine pests of apple. Here, a field survey of apples in a noncommercial setting in Washington and experiments testing whether earlier apples are more suitable than later apples for Washington-origin R. pomonella were conducted. Early season ‘Gala’ and ‘Golden Delicious’, midseason ‘Red Delicious’, and late-season ‘Granny Smith’ and ‘Fuji’ were exposed to flies reared from early and midseason apples in laboratory experiments and to wild flies in apple trees in field experiments. The field survey provided evidence for softer apples being more infested and suitable. Likewise, most of the laboratory and field experimental data showed that ‘Gala’ and ‘Golden Delicious’, two of the softer apples tested, produced the most larvae and were most suitable, regardless of whether test apples were conventional or organic and ripe or unripe. To optimise fly trapping protocols in Washington, trapping in early apple trees near ‘Gala’ and ‘Golden Delicious’ orchards should be prioritised over trapping near mid- or late-season apple cultivar orchards.

The unimodal latitudinal pattern of K, Ca and Mg concentration and its potential drivers in forest foliage in eastern China

Potassium (K), calcium (Ca), and magnesium (Mg) are essential elements with important physiological functions in plants. Previous studies showed that leaf K, Ca, and Mg concentrations generally increase with increasing latitudes. However, recent meta-analyses suggested the possibility of a unimodal pattern in the concentrations of these elements along latitudinal gradients. The authenticity of this unimodal latitudinal pattern, however, requires validation through large-scale field experimental data, and exploration of the underlying mechanisms if the pattern is confirmed. Here, we collected leaves of common species of woody plants from 19 montane forests in the north-south transect of eastern China, including 322 species from 160 genera, 67 families; and then determined leaf K, Ca, and Mg concentrations to explore their latitudinal patterns and driving mechanisms. Our results support unimodal latitudinal patterns for all three elements in woody plants across eastern China, with peak values at latitude 36.5 ​± ​1.0° N. The shift of plant-functional-type compositions from evergreen broadleaves to deciduous broadleaves and to conifers along this latitudinal span was the key factor contributing to these patterns. Climatic factors, mainly temperature, and to a lesser extent solar radiation and precipitation, were the main environmental drivers. These factors, by altering the composition of plant communities and regulating plant physiological activities, influence the latitudinal patterns of plant nutrient concentrations. Our findings also suggest that high leaf K, Ca, and Mg concentrations may represent an adaptive strategy for plants to withstand water stress, which might be used to predict plant nutrient responses to climate changes at large scales, and broaden the understanding of biogeochemical cycling of K, Ca, and Mg.

Impact of plastic mulching and residue return on maize yield and soil organic carbon storage in irrigated dryland areas under climate change

The widespread adoption of returning crop residue to regulate soil properties and improve yields has been well-established. However, the long-term effects of residue return on soil organic carbon (SOC) and yield in film-mulched farmland, especially under future climate conditions, are still unclear. To address this issue, we utilized the SPACSYS model, which was calibrated with 5-year (2016–2020) field experiment data from various mulching and fertilization treatments in the Hetao irrigation district in northwest China. Furthermore, we investigated the impacts of climate change on SOC storage in 20 cm and maize yield with calibrated SPACSYS model driven by 27 global climate models under two climate scenarios (SSP245 and SSP585) from CMIP6. We considered multiple combinations of agronomic options: five residue return levels (R1: 20%; R2: 40%; R3: 60%; R4: 80%; R5: 100%) and three return depths (D1: surface applied; D2: 10 cm; D3: 20 cm) to evaluate the effects of residue return on SOC storage and yield. We found that yield at a high fertilization rate with transparent film mulching (HT) would increase by 10.1% under SSP245, 15.3% under SSP585 in 2021–2060 compared to the baseline (1981–2020). By contrast, the yield would decrease by 5.8% under SSP245, 21.7% under SSP585 in 2061–2100. SOC storage under HT would decrease by 9.4–12.7% and 18.4–41.4% in 2021–2060 and 2061–2100, respectively. We determined that the optimal treatment involves returning 100% of residue to a depth of 20 cm (R5D3). Under R5D3, yield showed an increasing trend in both two future periods; the SOC storage decreased by 4.8–6.8% in 2021–2060 and increased by 3.9–16.1% in 2061–2100 compared to the baseline under HT. Our results highlight the potential of residue return to increase yield while maintaining soil health when film mulching is adopted.

On the Total Load Calculation in Combined Urban Sewer Conduits

Background: Particle size of sediment is necessary to design and operation of sewer systems. In this regard, calculation of the equivalent particle diameter (EPD) is of important to determine the particle Reynolds number (Ret ) as well as total load calculation. Methods: In this research work, 5 different particle diameters (i.e. d35, d50, d65, dm and deff) have been used in three famous total load calculation methods for calculating the best EPD. For this goal, a field experimental data has been collected at the entrance grit chamber of wastewater treatment plant (WWTP) of Khomein city, Iran. The total load of the sediments has been measured and the results compared with the total loads calculated by the three famous total load computation methods (i.e. Graf & Acaroglu method, Laursen method and Yang & Lim method) by using the particle diameters. Results: The results show that the methods estimate the total load of sediments with the relative errors of 4.25, 10.80 and 1.26 by using dm, d35 and d65 as the EPDs, respectively. Also, a simplified and improved correction factor has been developed and the results show that by applying the correction factor the relative errors of the methods decrease and they are equal to 10.34, 3.45 and 496.5, respectively. The improvement of the mentioned total load methods is equal to are 82.70%, 93.10% and 34.80%, respectively. Conclusion: The proposed correction factor can be applied for the standard deviation between 2.5-4.7 and the median particle diameter between 0.84-2.90 mm.

Critical Technical Problems of 3D GPR Road Cavity Detection Based on Vast Field Experiment Data Analysis

Utilizing 3D GPR to conduct road cavity detection can well prevent the occurrence of collapse. However, the problems existing in the application of 3D GPR in road cavity detection are not very clear. Hence, we have carried out more than 1000 km road cavity detection work and summarized the main points of data acquisition, processing and interpretation in engineering applications. The main points we found are related to the complexity of the pavement environment and the diversity of above and below ground interference sources, which are difficult to obtain in laboratory experiments. Then the solutions and ideas are put forward, aiming at accelerating the development of 3D GPR cavity detection technology in the field of engineering application.

Increasing heavy rainfall events and associated excessive soil water threaten a protein-source legume in dry environments of West Africa

The intensification of the hydrological cycle has increased heavy rainfall and drought events in a changing climate. However, compared to drought, the impacts of heavy rainfall on crop production are under-studied. Using field experimental data and a calibrated crop model CYGMA, we showed that excessive soil water associated with heavy rainfall events is having a detrimental effect on cowpea yields, even in the dry environments of West Africa where cowpea is an important, protein-rich cash crop. Cowpea yields are susceptible to heavy rainfall in areas with poorly drained soils, and to drought in soils that have a low water-retention capacity. The crop model captured of the main characteristics of the observed development, growth, and yield, as well as the characteristics of root-zone soil water contents and how they vary by soil type. The analysis of d4PDF factual and counterfactual climate model simulations revealed that heavy rainfall events associated with anthropogenic climate change have increased in recent decades, and that they are projected to increase in future. Further, changes in seasonal rainfall and the number of dry days would be largely absent from CMIP6 climate projections by mid-century. Reductions in cowpea yields due to excessive soil water is projected to become more frequent, and the potential damage in a 1-in-100 extremely wet year would be comparable to the damage currently experienced in droughts, irrespective of soil types. Simulations of the projected damage due to drought show that the situation will be similar to current levels, with drought remaining a major climate hazard. However, excessive soil water is projected to be a serious threat to food security in the region. Our findings indicate that, even in dry environments, cropping systems need to be implemented in order to reduce the susceptibility of soils to both drought and excessive soil water.

To trawl or not to trawl: Questioning core assumptions of trawl placement choice in fisheries acoustics surveys

In acoustic-trawl surveys, acoustic echosign needs to be biologically characterized, and if done correctly, will lead to accurate biomass estimates, robust stock assessments, and ensuing healthy fish stocks. However, methods for validating the core assumption that trawl placement choice yields a representative sample of the associated echosign (given an ideal net) are rarely described. Therefore we detail several such methods, using Pacific hake as a case study, exploiting both historic survey data as well as additional field experimental data. Specific methods focused on validation of (1) trawl effort spatially matching backscatter distributions, (2) trawled-location backscatter amounts matching all-location backscatter amounts, (3) trawled depth matching backscatter depth, and (4) spatial homogeneity of fish length within an aggregation. Application of the methods to the adult Pacific hake survey generally validated the assumption that trawl placement choice yielded a representative sample of the associated echosign, except for two instances. The backscatter from fished aggregations was greater than that from general aggregations in one of the two survey years analyzed. In addition, experimental field data detected a slight but significant trend of longer fish in the offshore portion of an aggregation. Neither of these occurrences are expected to yield a bias in the biomass estimate of Pacific hake.

Assessing the influence of water fertilizer, and climate factors on seed cotton yield under mulched drip irrigation in Xinjiang Agricultural Regions

Cotton is an important raw material, and its production has a profound impact on global economic and social development. Developing a further understanding of the optimal growing environment for cotton would help establish a reasonable management system and improve seed cotton yield. In the present study, we established a water-fertilizer coupling mathematical model based on the comprehensive analysis of existing field experimental data; this model was used to determine the optimal water and fertilizer requirements when the seed cotton yield reached a maximum in different regions. In addition, the grey relational degree between the climate factors and seed cotton yield during the cotton-growing period were analyzed using grey relational analysis, including average air pressure (AAP), sunshine duration (SD), average relative humidity (ARH), average maximum temperature (MAXT), average temperature (AT), average minimum temperature (MINT), and rainfall (RF). The results showed that the optimal irrigation levels were determined as 675.61 mm for southern Xinjiang and 345.59 mm for northern Xinjiang. In addition, the ideal fertilizer application rates (N −P −K) were found to be 150.26–115.66–76.28 kg/ha for southern Xinjiang and 290.32–162.57–72.22 kg/ha for northern Xinjiang. These optimal practices resulted in yield potentials of 7140.30 kg/ha for northern Xinjiang and 8693.69 kg/ha for southern Xinjiang. Simultaneously, AAP, MAXT, SD, AT, and ARH exhibited a strong influence on seed cotton yield in Xinjiang. Conversely, RF and MINT had minimal impact on seed cotton yield. This research determined the optimal water and fertilizer requirements and highlighted the significance of climate factors in Xinjiang agricultural regions. These findings offer a scientific foundation and practical guidance to enhance seed cotton yield, water and fertilizer efficiency, optimize management practices, and address climate fluctuations during the cotton-growing season.

Genome survey and identification of key genes associated with freezing tolerance in genomic draft of hexaploid wild Camellia oleifera

ABSTRACT Camellia oleifera Abel. is the dominant woody oil crop under significant development in China. Wild C. oleifera in Lu Mountain is a valuable genetic resource with strong freezing tolerance. With high-throughput sequencing, the genome of wild C. oleifera in Lu Mountain was analysed and 700.3 Gb clean reads were obtained. The genome of wild C. oleifera was estimated as allohexaploid, and its haplotype genome size was about 2.69 Gb-2.79 Gb, with repeat content of 63.01%-73.02% and heterozygosity of 6.30%-7.43%, belonging to a very complex genome. The genomic draft was assembled that contained a total of 6,952,303 scaffolds with N50 length of 1.23 kb, and the overall length was 2.39 Gb with GC content of 40.87%. In the genomic draft, 1,104,618 SSRs were identified; scaffold1096012 and scaffold1779458 were identified as key genes associated with freezing tolerance combined with the transcriptome data of field plus lab experiments. In this study, the genomic background of hexaploid wild C. oleifera in Lu Mountain was revealed. This lays the foundation for obtaining the high-quality chromosome-level reference genome of wild C. oleifera. The identification of SSRs and key genes associated with freezing tolerance may contribute to the efficient exploration and utilisation of this genetic resource.

Optimizing Nitrogen Fertilization to Enhance Productivity and Profitability of Upland Rice Using CSM-CERES-Rice.

Nitrogen (N) deficiency can limit rice productivity, whereas the over- and underapplication of N results in agronomic and economic losses. Process-based crop models are useful tools and could assist in optimizing N management, enhancing the production efficiency and profitability of upland rice production systems. The study evaluated the ability of CSM-CERES-Rice to determine optimal N fertilization rate for different sowing dates of upland rice. Field experimental data from two growing seasons (2018-2019 and 2019-2020) were used to simulate rice responses to four N fertilization rates (N30, N60, N90 and a control-N0) applied under three different sowing windows (SD1, SD2 and SD3). Cultivar coefficients were calibrated with data from N90 under all sowing windows in both seasons and the remaining treatments were used for model validation. Following model validation, simulations were extended up to N240 to identify the sowing date's specific economic optimum N fertilization rate (EONFR). Results indicated that CSM-CERES-Rice performed well both in calibration and validation, in simulating rice performance under different N fertilization rates. The d-index and nRMSE values for grain yield (0.90 and 16%), aboveground dry matter (0.93 and 13%), harvest index (0.86 and 7%), grain N contents (0.95 and 18%), total crop N uptake (0.97 and 15%) and N use efficiencies (0.94-0.97 and 11-15%) during model validation indicated good agreement between simulated and observed data. Extended simulations indicated that upland rice yield was responsive to N fertilization up to 180 kg N ha-1 (N180), where the yield plateau was observed. Fertilization rates of 140, 170 and 130 kg N ha-1 were identified as the EONFR for SD1, SD2 and SD3, respectively, based on the computed profitability, marginal net returns and N utilization. The model results suggested that N fertilization rate should be adjusted for different sowing windows rather than recommending a uniform N rate across sowing windows. In summary, CSM-CERES-Rice can be used as a decision support tool for determining EONFR for seasonal sowing windows to maximize the productivity and profitability of upland rice production.

Modification of the RothC model to evaluate the inconsistent effect of conservation tillage on SOC stock and a suggestion of a national-scale assessment framework

Simulation of conservation tillage effect on soil organic carbon (SOC) stock on the national scale is essential for Tier 3 level greenhouse gas inventory in the agricultural sector. However, the conservation tillage effects varied depending on different soil conditions, potentially leading to inaccurate national assessments. This study aimed to propose a framework for estimating the national scale impact of conservation tillage on SOC. As even in the most commonly used SOC dynamic model, the Rothamsted Carbon Model (RothC), does not reflect the conservation tillage effect in an explicit way, we modified it by developing the tillage rate modifiers (TRMs). First, we investigated the conditions for the inconsistent conservation tillage effects using the decision tree analysis based on 210 field experiment data from the mid-latitude region. The results highlighted that soil sand content and the existing SOC stock were the main factors driving the inconsistencies. After we categorized into four distinctive conditions, the TRMs for each condition were parameterized using a genetic algorithm. The average TRMs were 0.88 in the soils with sand content >37.6 % and 1.58 in the soils with sand content ≤37.6 %, indicating that conservation tillage is more effective in coarse-textured soil, and there is a risk of decreasing SOC stock in the latter condition. Using the modified RothC model, a three-step national-scale simulation framework was suggested: compiling country-specific data, establishing baseline and conservation tillage scenarios, and modeling conservation tillage effects with uncertainty analysis. Our approach also defined the maximum conservation tillage area, factoring in local cropping systems and soil conditions. Our refined RothC model with TRMs provides a nuanced understanding of conservation tillage effects, emphasizing the role of soil characteristics. The proposed national-scale simulation framework offers a reliable tool for evaluating conservation tillage impact on SOC, ensuring more accurate greenhouse gas inventories in agriculture.

Can windcatcher's natural ventilation beat the chill? A view from heat loss and thermal discomfort

Windcatchers provide effective low-energy ventilation and summer passive cooling in temperate climates. However, their use in winter is limited due to significant ventilation heat loss and potential discomfort. Limited research has been conducted on quantifying windcatcher heat loss in cold climates, particularly through field studies. This study aims to evaluate the applicability of windcatchers in low-temperature conditions, with a focus on ventilation heat loss and thermal discomfort. Field experiments were conducted in Nottingham, UK, during an icy period. A 3D-printed prototype windcatcher and a test room were built and tested in such weather conditions. A Computational Fluid Dynamics (CFD) model validated against the field experimental data was employed to investigate the windcatcher's performance in a typical UK primary school classroom. The field experimental results indicate that the indoor airflow patterns are dynamic and continuously change with varying external wind conditions. Using static boundary conditions for ventilation analysis is inadequate, as it may lead to inaccurate predictions due to observed fluctuations and irregular airflow patterns. CFD modelling revealed significant over-ventilation in the classroom at external wind speeds of 3 m/s, despite being previously deemed as “satisfactory”, “adequate”, or “sufficient” ventilation. At wind speeds of 3 m/s or higher, the over-ventilation can cause a minimum 941.4 W heat loss, adding 4.7 kWh heating load and £1.6 electricity cost for a typical-sized single classroom during a 5-h occupied period. The research findings highlighted that control strategies should be introduced to reduce over-ventilation. Integrating heat recovery or thermal storage can enhance winter thermal conditions.

Optimizing cropping systems to close the gap between economic profitability and environmental health.

Supporting food security while maintaining ecosystem sustainability is one of the most important global challenges for humanity. Optimization of cropping systems is expected to promote the ecosystem services of agroecosystems. Yet, how and why cropping system influences the trade-offs between economic profitability and multiple ecosystem services remain poorly understood. We investigate the influence of six cropping systems on trade-offs between economic profitability and multiple ecosystem services after considering 36 agricultural ecosystem properties using field experiment data from 2020 to 2022. We show that designing cropping system is a critical tool to closing the gap between ecosystem sustainability and commercial profitability. Cropping system with three harvests within 2 yr had higher performance in overall ecosystem multiple services through enhancement of supporting, regulating, and economic performance without compromising provisioning compared with four other systems. These systems diminished the trade-off among multiple services, resulting in a 'win-win' situation for economics and multiple services. By contrast, the monoculture and double cropping systems lead to a strong trade-off between pairwise services including ecosystem health and profitability. Our work illustrates the substantial potential of rotation systems with three harvests within 2 yr in enforcing ecosystem services and closing the trade-offs among multiple agricultural ecosystem services.

Feature-based magnetotelluric inversion by variational autoencoder using a subdomain encoding scheme

Magnetotelluric (MT) data inversion aims to reconstruct a subsurface resistivity model that minimizes the discrepancy between inverted and measured electromagnetic data. Conventional pixel-based minimum-structure inversion often yields a smoothed-out reconstruction with a relatively low resolution. A priori geophysical knowledge can be embedded into inversion and improve the reconstruction resolution through proper reparameterization. However, existing reparameterization approaches, such as model-based and parametric transform-based inversion, have limited ability to incorporate various a priori information. The effectiveness of existing deep generative model-based inversion algorithms is still debatable when applied to scenarios with complex backgrounds. We develop a feature-based MT data inversion method based on a variational autoencoder (VAE) with a subdomain encoding scheme. Instead of encoding the entire domain of an investigation, we adopt a 1D subdomain encoding scheme to encode the 1D resistivity-depth models using a single VAE. The latent variables for the 2D model are a combination of the latent variables for 1D models, and the encoded region of interest (ROI) can be flexibly determined. The latent variables of ROI and the pixels outside the ROI are simultaneously inverted using the gradient-descent method. Our 1D subdomain encoding scheme reduces the complexity and diversity of the data set, and it can flexibly embed a priori knowledge with various uncertainties. Synthetic data inversion and inversion of the Southern African Magnetotelluric Experiment field data validate our method’s ability to effectively improve inversion accuracy and resolution.

Numerical simulation and diagnosis geotechnical parameters of historical buildings in Najran City, Kingdom of Saudi Arabia

This research aims to assess geoenvironmental risks and identify the primary deterioration drivers in ancient buildings in Najran City, utilizing various analytical tools to help make informed judgments. The samples extruded from historical buildings were examined using field inspection, experimental data, SEM, EDX, and XRD analyses, in addition to lab and field observations and meteorological data. The dissolution of clay minerals and salt crystallization are the key contributors to the degradation and cracking of historical buildings in Najran City, according to lab and field observations. When the daytime high temperature surpasses 44 °C, wind erosion and humidity might cause continuous wetting–drying cycles on the investigated building surfaces. Test results indicated that the average unconfined compressive strength of the extruded earthen wall samples was 2 MPa and the water absorption was within the upper allowed limit (i.e., 15%). A finite element model of a typical earthen historical building was developed using PLAXIS 3D software to assess the behavior and nonlinear response of the silty sand soil layer underlying the building and the earthen historical buildings themselves using a plastic material model. The field observations confirm the results of the simulation, which clearly explained the failure mechanism. The integrated geotechnical and numerical simulations could provide insights for assessing geoenvironmental risks, identify the primary deterioration drivers in ancient buildings, and provide an understanding of material qualities and failure causes not only in the studied area but in other similar regions elsewhere.

Hydrus-1D Model for Simulating Phosphorus Transport in Paddy Crop Irrigated with Alternate Wetting and Drying Practice

ABSTRACT The alternate wetting and drying (AWD) irrigation practice involves soil moisture stress-based irrigation scheduling which possibly affects the rice field’s phosphorus (P) transport and balance components. The focus of this study was to understand P-transport under AWD irrigation and estimate the associated balance components in rice using a solute transport model, Hydrus-1D. In this study, rice was cultivated in 30 lysimeter plots with three irrigation treatments (no soil water stress/NS, mild soil water stress/400 cm/MS, and severe soil water stress/750 cm/SS) and each treatment was replicated 10 times. The field experimental data were used to simulate P transport with the help of the Hydrus-1D model. The Hydrus-1D model was calibrated and validated for P transport within the soil matrix under AWD irrigation practice. The P distribution was mostly concentrated within the top puddled soil layer (0–20 cm) and did not move to the deeper soil layers. The simulated results indicate that the recommended dose of P with 3–5 cm ponding water depth will increase the P availability for rice and reduce the P leaching loss in AWD irrigation practice. The Hydrus-1D model can be used as a management tool to determine the appropriate P application rate and ponding water depth irrigated with AWD for which the required P nutrients are sufficiently available to rice and the P loss to the environment can be controlled for sustainable rice production.

Convolutional and fourier neural networks for speckle denoising of wrapped phase in digital holographic interferometry

Owing to the unique non-Gaussian statistics and non-stationary properties, speckle denoising in digital holographic interferometry measurements is essential and challenging. Traditional Fourier-based frequency-domain filtering and deep-learning-based (DL-based) spatial-domain speckle denoising methods have realised significant achievements. However, these methods use different mechanisms and are applied separately. A deep learning-based algorithm combining the aforementioned methods is proposed, which simultaneously extracts features in the spatial and frequency domains via convolutional and Fourier neural networks (CFNN) for enhanced speckle denoising and minimising the phase unwrapping process error. Compared with the convolutional neural network (CNN), which provides spatial-domain features for the framework architecture to layer extraction operation, the Fourier neural operator (FNO) developed from traditional Fourier filtering provides the frequency-domain features. The differential gradient map of denoised wrapping is used as a loss function, further improving the smoothness of the denoised wrapped images and the subsequent phase-unwrapping performance. Compared to other algorithms, the proposed method achieves a relative improvement of 7.1% on the testing dataset and 5.0% on digital holographic deformation field experimental data. It can be extended to more fields of optical information processing, since it unifies the feature extraction and reconstruction in the spatial-domain and the frequency-domain in the same deep learning model.

Changing maize hybrids helps adapt to climate change in Northeast China: revealed by field experiment and crop modelling

Adopting new cultivars is an effective strategy to partially offset the negative effects of climate change on crop yields. We integrated the regional agro-meteorological observations, site-level field experiment data, and a process-based crop model to assess the effects of maize hybrid management on grain yield at meteorological sites across Northeast China. On a regional scale, for per 1 ℃ increase in Tmin (Tmax) at the agro-meteorological stations, the length of the entire actual growing period for maize was shortened by 3.6 (2.3) days per year. Without changing maize hybrids, maize grain yield could be in jeopardy. Adopting hybrids with relatively longer growing period could take full advantage of the regional warming trend and hence improve the maize grain yield. In this study, we found that adopting novel hybrids (bred through trait selection and propagation during the past four decades) could improve maize grain yield in Northeast China. This conclusion was supported by the two-year field experiment that used nine prevailing newly-bred hybrids and the well-validated crop modeling at 74 locations covering a 60-year period (from 1961 to 2020). Compared with using the same hybrid, changing hybrid adapted the maize production to climate change in Northeast China; and the regional maize grain yield was increased by 5.4%, 7.4%, 6.8%, and 10.3%, respectively, due to changes in, respectively, solar radiation, Tmax, Tmin, and precipitation. The improvement in maize grain yield became more evident as latitude increased, especially at locations to the north of 42.5ºN. In the future, changing hybrid may increase the region-simulated maize grain yield by 6.2 and 7.2% under the scenarios of SSP-126 and SSP-585, respectively. In sum, adopting newly-bred maize hybrids, especially those with relatively long growing periods, would be an effective measure to adapt regional maize production to climate change in Northeast China.

Robust Statistical Processing of Long-Time Data Series to Estimate Soil Water Content

AbstractThe research presented in this paper aims at providing a statistical model that is capable of estimating soil water content based on weather data. The model was tested using a long-time series of field experimental data from continuous monitoring at a test site in Oltrepò Pavese (northern Italy). An innovative statistical function was developed in order to predict the evolution of soil–water content from precipitation and air temperature. The data were analysed in a framework of robust statistics by using a combination of robust parametric and non-parametric models. Specifically, a statistical model, which includes the typical seasonal trend of field data, has been set up. The proposed model showed that relevant features present in the field of experimental data can be obtained and correctly described for predictive purposes.