Soil Field Capacity Research Articles

Model ensemble techniques of machine learning algorithms for soil moisture constants in the semi‐arid climate conditions

AbstractIn recent years, the use of prediction models based on intelligent algorithms has become widespread in soil science. However, each algorithm has advantages and disadvantages, and variable results can occur on different datasets. The evaluation of ensemble techniques for solving these problems is the current approach. Water problems will arise due to global warming, and soil water will become more important. This study aims to evaluate the predictive accuracy of different machine learning algorithms (support vector machine regression (SVR), random forest (RF), artificial neural network (ANN), and multivariate linear regression (MLR)) and ensemble techniques (equal weight [EQ], Bates–Granger‐BG), Granger–Ramanathan (GR), Akaike information criterion (AIC), and Bayesian information criterion (BIC)) on the field capacity (FC), wilting point (WP) and available water content (AWC) of soils. As a result, higher prediction accuracy was obtained with the RF algorithm than with the value machine learning algorithm in the estimation of moisture constants. The coefficients of determination (R2) obtained for the prediction of FC, WP, and AWC via the RF algorithms were 0.624, 0.759 and 0.641, respectively. MLR had the highest error rate. Among the ensemble techniques, GR was the most successful. Lin's concordance correlation coefficient (LCCC) values obtained from the estimation of FC, WP, and AWC with the GR model were 0.801, 0.894, and 0.801, respectively. The root mean squared error (RMSE) and mean absolute error (MAE) values obtained in the estimation of the available water content with the MLR algorithm were 1.905 and 1.435, respectively, whereas these values were 1.173 and 0.767, respectively, when the GR model was used. As a result of the present study, better predictive results were obtained with ensemble techniques instead of evaluating the algorithms individually.

Response Surface Methodology for Optimizing Water and Fertilizer Requirements for Maize (Zea mays L.) Growth in Sandy Soil

AbstractTo foster sustainable agricultural practices, the utilization of irrigation, fertilizers, and recycled soil enhancements is essential, particularly in regions with limited resources. This investigation sought to ascertain the optimal water and fertilizer prerequisites for the cultivation of maize by employing Response Surface Methodology (RSM) in arenosol enriched with spent mushroom substrate (SMS) in a controlled pot experiment. The experimental treatments were determined using the Central Composite Design based on varying levels of irrigation (50%, 75%, and 100% of soil field capacity), nitrogen (0, 1, and 2 g pot−1), and SMS (0, 5, and 10%, v/v). The investigation's findings demonstrated that augmented irrigation and nitrogen fertilization positively influenced all agronomic traits, as well as nitrogen concentrations in the roots, shoots, and soil. SMS increased shoot fresh weight, shoot dry weight, leaf area per plant, and dissolved and total soil organic carbon. The optimal levels of irrigation, nitrogen fertilizer, and SMS to improve agronomic attributes were determined to be 81.43–97.80%, 1.38–1.69 g pot−1, and 5.77–8.48%, respectively. The optimal amounts for NPK retention in soil and plant uptake were 69.50–98.00%, 1.20–1.98 g pot−1, and 4.72–9.74%, respectively. The study concluded that irrigation and nitrogen levels had a greater impact on optimizing maize growth response than SMS. However, SMS was found to be more effective in increasing plant biomass due to its enhancement of both dissolved and total soil organic carbon. Furthermore, the interaction of irrigation and mineral nitrogen with SMS improved soil nutrient retention, plant uptake, and plant biomass productivity.

Dynamic and empirical methods for field capacity estimation in fine textured soils with a coarse interlayer

Field capacity (FC) is an important soil hydraulic concept in soil science and irrigation management. It is generally determined from soil water content in a soil layer when soil profile reaches a steady pressure head or negligible drainage flux from an initially saturated soil. However, the proposed criteria are mainly tested for uniform soils and vary with soil textures. To quantify FC in layered soils, a Richards equation-based model was used to describe water flow in fine-textured soils with a coarse interlayer. With calibrated soil hydraulic parameters for loam and sand from infiltration measurements, drainage from saturation was simulated in the loam with a sand interlayer. A relative drainage rate (δ) was defined as a function of water storage and drainage flux to analyze soil water status at FC. Soil water content in the upper loam layer of layered profiles was improved compared with that in the uniform loam, which was negatively correlated with buried depth but positively correlated with thickness of the sand layer for a specified δ. Under different buried depths and thicknesses, soil water content decreased with the decline of δ and decreased rapidly as δ reduced to 1 % d−1. The drainage flux at δ = 1 % d−1 changed within a range of 0.056–0.26 cm d−1, and soil water content reached to 0.278–0.346 cm3 cm−3, which accounted for 70–87 % of the saturated water content of loam. Although the FC in the upper fine-textured soil layer varied for different buried depths and thicknesses of coarse interlayer, the proposed dynamic method is reliable and universal to estimate the FC in the above layered soils at δ = 1 % d−1. An empirical equation was also developed to calculate the FC in fine-textured soils with different buried depths and thicknesses of a coarse interlayer based on the critical δ value.

Prediction of Soil Field Capacity and Permanent Wilting Point Using Accessible Parameters by Machine Learning

The field capacity (FC) and permanent wilting point (PWP) are fundamental hydrological properties critical for assessing water availability within soils, rather than direct measures of soil health. Due to the challenges associated with their field measurement, alternative assessment methods are necessary. In this study, global-scale accessible soil data were retrieved from the world soil database called the World Soil Information Service (WoSIS), and artificial neural network (ANN) and gene-expression programming (GEP) algorithms were used to predict soil FC and PWP based on easily obtainable parameters from the database. The best-fit variable combination for FC (longitude, latitude, altitude, sand content, silt content, clay content, and electrical conductivity) and PWP (best-fit FC combination plus pH) modeling was determined. Both ANN and GEP showed greater accuracy than linear-based models in simulating the FC and PWP from the best-fit variables. The mean absolute error (MAE) was reduced by 51.54% for the FC and 56.38% for the PWP by the ANN model, compared with the linear model used in the previous literature. The normalized root mean square error (NRMSE) evaluation indicated that the ANN model performed best for PWP prediction (NRMSE of 19.9%), while the GEP model was superior for FC prediction (NRMSE of 29.9%). Between the ANN and GEP models, the ANN model showed a slightly higher model of interpretability; however, the GEP model exhibited a similar or better ability to avoid large error, based on the error distribution. Overall, our results demonstrated that machine learning is effective in predicting the FC and PWP from easily accessible data from WoSIS, and the GEP model is more preferable for FC and PWP modeling.

Pedotransfer functions development for modeling FC and PWP using Vis-NIR spectra combined with PLSR and regression models

The utilization of the soil pedotransfer functions (PTFs) developed based on the basic soil propertied is an alternative, fast, cost-effective and applicable approach for the prediction of field capacity (FC) and permanent wilting point (PWP). In addition, the Visible–Near-Infrared (Vis-NIR) spectra in soil science has gained prominence due to its practicality and relevance. In this paper, we used the data of the soil PWP and FC of 135 soil samples, easily measurable soil properties and Vis-NIR spectroscopy. The multiple linear regression (MLR) model was utilized to formulate PTFs model and Vis-NIR spectroscopy combined with MLR and partial least-squares (PLSR) was used to develop Spectrotransfer Function (STF). Results showed that among the easily measurable soil properties, particle-size diameter (dg) with Beta of −0.72 and −0.63 the most influential parameters for predicting FC and PWP, respectively, followed by the clay content. Developed PTFs for both FC and PWP with a R2 of 0.71 and 0.68, respectively, had a better performance than other previous developed PTFs. Results also revealed that the PLSR with a higher R2 (0.81) and lower RMSE (4 %) significantly performed better in comparison to STF for both FC and PWP prediction.

Effects of soil moisture and an AC-electric field on the phytoremediation of the Cd-contaminated soil

ABSTRACT Phytoremediation enhanced by electric field has been considered a green and low-cost technology for remediating heavy metal-contaminated soils. Soil moisture is a main environmental factor that affects Cd availability in the soil. However, the effects of soil moisture and AC-electric field on the remediation efficiency of willow (Salix spp.) and S. Alfredii interplanted together remain unclear. In the present study, we designed four treatments (60% soil field capacity, 60% soil field capacity + 0.5 V·cm−1 AC, 100% soil field capacity, 100% soil field capacity + 0.5 V·cm−1 AC) to explore the impacts of soil moisture and AC-electric field on soil Cd availability and Cd accumulation in plants. The results showed that the application of an AC-electric field significantly increased soil Cd availability by 20.9% and 10.8% under both 60% and 100% soil field capacity, respectively. Both high water with and without AC-electric field treatments reduced the proportion of acid-extractable and reducible Cd of soil but increased the proportion of residual Cd. Compared with the control, an AC-electric field with 60% soil field capacity significantly enhanced the biomass of S. Alfredii shoots by 31.2% and increased Cd accumulation in willow leaves and S. Alfredii shoots by 14.6% and 32.3%, respectively. In addition, the biomass production of willow was significantly enhanced but the uptake of Cd by willow was dramatically decreased under an AC-electric field with high water treatment. Therefore, these results suggest that the AC-electric field combined with 60% soil field capacity may be a more promising remediation technique to clean up the Cd-contaminated soil.

Can precipitation intermittency predict flooding?

A mystery has emerged as to why patterns of increasing extreme rainfall have not been accompanied by similar levels of flooding, garnering growing attention given concerns over future flood risks. Antecedent moisture conditions have been proposed as the missing explanatory factor. Yet, reasons for moisture variability prior to flooding remain largely unstudied. Here, we evaluate the potential utility of precipitation intermittency, defined as the dry spell length prior to a flood, to explain the variability of flooding over 108 watersheds from 1950 to 2022. Flood magnitude is shown to be sensitive to intermittency, particularly in arid and semi-arid regions (PET/P > 0.84) and for basins with low soil field capacity (<0.31 m3/m3). Following extended dry spells >20 days, floods are only possible from the most intense storms, whereas a wider range of storms can produce flooding for shorter intermittency. The flood probability decreases by approximately 0.5 % for each additional day of dry spell, with overall flood probabilities being up to 30 % lower following extended dry periods. These results underscore the potential utility of precipitation intermittency for diagnosing current and future flood risks.

Effects of water and exogenous glucose on apple seedling growth and soil enzyme activity in semiarid area.

Soil water and organic carbon (C) are key factors affecting the growth and development of apple seedlings. The objective of the study was to investigate the effects of different soil moisture and glucose supplies on apple seedling growth and soil enzyme activities. We hypothesized that the growth of apple seedlings was affected by soil water and C content through their effects on root structure, plant physiological properties and soil enzymatic activities. A pot experiment consisting of nine treatments was set up, including three water treatments with soil moisture contents at 75-85% (normal irrigation, CK), 65-75% (light water stress, LS), and 55-65% (mild water stress, MS) of the soil field capacity, in combination with three glucose treatments with carbon/nitrogen (C/N) ratio of 7.5 (C1, no adding glucose), 10 (C2) and 15 (C3), respectively. Results showed that the LSC2 treatment significantly increased plant height by 7%, stem diameter by 5% and leaf area by 17%, as compared with LSC1. Also, LSC2 significantly increased root dry weight, root vitality and soil enzyme activities. Moreover, results of leaf photosynthetic, malondialdehyde (MDA), peroxidase (POD), superoxide dismutase (SOD) and proline contents also proved that adding glucose improved the drought resistance of plants. LSC2 treatment is more conducive to the growth of apple seedlings, and application of carbon has a good alleviation effect on plant water stress. The study demonstrated that addition of exogenous glucose alleviated light water deficiency, significantly affected root vitality, and promoted apple seedling growth. © 2024 Society of Chemical Industry.

Response of Grafted Olive (Olea europaea L. Cv. Coratina) to Water Deficit Conditions

Drought is one of the most damaging abiotic stresses; water deficit problem is increasingly occurring due to global climate change and negatively affects crop growth and productivity. Grafting on tolerate rootstocks is a promise approach to mitigate negative impacts of drought stress and ensure production sustainability. The present study was carried out to investigate the effect of water deficit stress on Coratina olive plants grafted on the following cultivars as rootstocks (Coratina, Koroneiki, Manzanillo, Picual and Sorani). Three water levels based on soil field capacity (FC) (100, 50% and 25% of FC) were used for water deficit treatments. Water deficit decreased shoot growth, stem diameter, leaves number and area, shoot and root weight. Leaf analysis showed marked decrease in total chlorophyll content while proline, total sugars and phenolic content increased with increasing water deficit level. The studied grafting combination differed in their response to water deficit treatments; Coratina grafted on Sorani and Koroneiki recorded higher values of growth parameters and accumulated higher amount of osmolytes (proline and total sugars) and phenolic compared to other grafted olive plants.

Estimating Water Supply Requirement on Pahang Soil Series to Achieve a Minimal Water Consumption for Resource Efficiency in Agroecology

Agroecology has ten elements that can be implement in the agricultural activities and food systems to achieve a more sustainable future in producing and consuming foods. Water saving is one of the important attributes of agroecological practices. In this study, we are investigating soil series information provided by the Department of Agriculture, Malaysia. There are 34 soil series identified in Pahang state. The existing information provided with the soil series do not guide the implementation of irrigation management in terms of water consumption requirement for plantation land management. The current study converts soil series information into soil textures. There are seven soil textures determined in Pahang states. They were Clay, Silty Clay, Clay Loam, Silty Clay Loam, Sandy Clay Loam, Loam, and Sand. Soil textures were used to estimate soil hydraulic properties to represent the soil series. Soil field capacity, permanent wilting point, and plant available water were determined. Water infiltration simulation was carried out on all soil textures, and it was found silty clay loam (Gugut Series, Setol Series) was one of the best soil textures in terms of water infiltration time, and water retention in the soil. The information derived from the current study can be used for land management planning to estimate the irrigation water supply needed, which will be useful for cost estimation and water resource management.

Soil Water Stress Effects on Potato Tuber Starch Quality Formation

AbstractSoil water stress has a significant impact on crop physiology, however, the specific response of starch quality formation in potato tubers remains unreported. Here, two potato (Solanum tuberosum L.) varieties, one with high, and the other with low tuber starch content, were grown in pots under three different soil water stress treatments, maintaining 75, 50 and 25% of soil field capacity, respectively. Soil water stress restricted potato plant growth and development, and severe stress reduced tuber yield by 47.8% relative to the control. It also inhibited tuber starch biosynthesis, which declined by 62.4% (AGPase activity) relative to the control. Furthermore, water stress reduced tuber starch accumulation by 23.6% (total starch content) relative to the control, and finally, it shortened the tuber starch gelatinization process by 1.44% (pasting temperature) compared to the control. These results reflect the soil water stress regulation mechanism on starch formation and potato tuber quality. Moreover, the study provides a scientific basis for breeding of varieties with high starch content, for improving starch quality and high-efficiency cultivation in dryland potato production.

Jasmonates Improve Drought Tolerance of Hordeum vulgare L. After Biochar Treatment

To investigate how and in what amounts biochar and methyl jasmonate can improve drought tolerance of barley. A two-year experimental study was conducted in a factorial randomized complete block design (n = 5) in the research greenhouse of Zanjan University, Iran, to investigate the possible effects of biochar and methyl jasmonate on some traits of winter barley under drought conditions. Two irrigation regimes, D0 (full irrigation in soil field capacity as control) and D1 (withholding irrigation immediately after flowering stage), three methyl jasmonate spray densities [0 (M0), 50 (M50), and 100 (M100) μM] and four levels of biochar in soil [0% (B0), 0.25% (B0.25), 0.5% (B0.5), 1% (B1) per soil weight] were used in this experiment. In this study, drought reduced two-year average leaf area (LA) by 96%, stomatal conductance (gs) by 84%, and photosynthetic water use efficiency (PWUE) by 64%. In addition, drought reduced chlorophyll-b by 1.5% and 81% and transpiration rate (Tr) by 2.5% and 78% in the first and second years, respectively. However, the application of biochar and methyl jasmonate improved all the traits studied in both D0 and D1 drought-treated plants. For most of the parameters studied, the optimal combination of biochar and methyl jasmonate that optimized water use efficiency and alleviated drought was 0.25% and 50 μM, respectively. The synergistic action of biochar and jasmonates improved the tolerance of barley to water stress.

Como utilizar o ponto de inflexão da curva de retenção de água e os atributos físicos do solo para previsão da capacidade de campo?

ABSTRACT Agricultural productivity is closely related to soil physical attributes, specifically those that affect the soil-water relationship, as the soil serves as the main water reservoir for plants. This research aimed to determine the field capacity for different soils, using equations based on the water retention curve. The database used included 150 soil profiles from studies published by other authors encompassing information related to textural classification, soil bulk density, particle density and soil water retention. The inflection point for each soil profile and the corresponding matrix potential were generated. Multiple correlations were established between volumetric moisture at field capacity and clay, silt and sand contents. The calculated inflection point can be an estimator of field capacity, what may facilitate and speed up the calculation of water availability.

Effect of Tragacanth Gum on Some Protective Mechanisms of Black Cumin (Nigella sativa L.) Under Drought Stress Conditions

The production of active oxygen species increases under drought stress conditions, which means that their removal or deactivation is out of plant power. Therefore, most plants in the face of drought stress require solutions adopted by farm managers. In order to investigate the effect of various irrigation regimes and anti-evapotranspiration effect of different concentrations of tragacanth gum on protective mechanisms of Nigella sativa L., a factorial experiment was conducted in a completely randomized design with three replications in the greenhouse of the Faculty of Agriculture, the University of Kurdistan in 2018. The experimental factors included irrigation at three levels of 100 (full irrigation), 70 (mild drought stress), and 40% (severe drought stress) of field capacity of soil and spraying with tragacanth gum at six concentrations of 0, 1.25, 2.5, 5, 7.5, and 10 g/L. The results showed that increasing the intensity of drought stress (irrigation reduction) resulted in increased levels of H2O2, MDA, SOD, POD, proline, soluble carbohydrates (water and alcohol soluble), and osmotic potential of the plant and reduced grain yield. The effect of different concentrations of tragacanth gum was differed at different levels of irrigation. In full irrigation, the concentration of 1.25 g/L was positive for all studied traits. In mild drought stress, the use of higher concentrations of tragacanth (up to 5 g/L) had the best efficiency, and concentrations greater than 5 g/L had opposite effects on the traits. In severe drought stress, the use of higher concentrations of tragacanth was beneficial and concentrations up to 7.5 g/L improved the studied traits, but concentrations of 10 g/L had a negative effect on these traits. Regarding that the application of appropriate concentrations of tragacanth gum improved the measured traits and increase grain yield under mild and severe drought stress by 11.6% and 28.2%, respectively, it may be introduced as a novel anti-evapotranspiration agent with natural origin and may be useful in areas with drought stress.

New procedure to estimate soil field capacity based on double ring infiltration data

Many models have been proposed to explain the water infiltration curve in soil, but according to the studies we have done so far, no model can simultaneously estimate the field capacity (FC) and soil hydraulic conductivity (K). The aim of this research is to present a new model that is defined based on the dynamic balance of water-soil-time and is able to estimate FC and soil hydraulic conductivity using cumulative field infiltration data. The studies were carried out in Varamin region with an area of 1370 square kilometers. For the penetration test, first, seven sides (A, B, C, D, E, F and G) were selected in 7 areas of agricultural land and with a double ring with an outer diameter (70 cm) and an inner diameter (30 cm) became. Also, to determine the soil texture, apparent specific gravity, PWP and FC, soil sampling was done at the depths of 0–10 and 20–30 cm. To determine FC moisture, pressure (−10 kPa) was used in a pressurized membrane device, and to determine volumetric moisture in PWP, sieved samples were placed on ceramic plates. Using a spray, the samples were wetted, so that they reached the saturation level, and the pressure was set to 1500 kPa. Examining the FC values showed that the maximum value of FC in site F (clay loam texture) is equal to 14.73 cm and the lowest value in site G (sandy clay loam texture) is equal to 6.8 cm by the model estimated by the results obtained in Laboratory confirmed. Meanwhile, the minimum saturated hydraulic conductivity of soil at site A was estimated to be 0.06 cm/min and the maximum at sites D and F was 0.28 cm/min. Time constant (Ksh) has shown completely different water infiltration rate in the first term of the model (Rfksh) compared to the second term of it (K), and no obvious relation has detected between them. But, their summation (k+Rfksh) was always equal to the maximum infiltration instantaneous rate (T→0). The research results showed that using the new model, the FC value varies between 6.8 and 14.73 cm. Also, the minimum and maximum instantaneous speed of water infiltration occurred in the site. An equal to 0.621 and in site D equal to 1.735 cm/min. The results of fitting the penetration model on cumulative penetration data showed that R2 varies between 0.994 and 0.999 and RMSE between 0.116 and 1.197. Therefore, the model is able to describe the penetration data well.

Green waste-derived compost (GWC) alleviates drought stress and promotes sugar beet productivity and biofortification

ABSTRACT Green waste-derived compost (GWC) is a valuable soil amendment for improving soil organic matter and decreasing waste products and potential pollutants. The study was carried out to evaluate the effect of GWC application on the yield and quality of sugar beet under deficit irrigation conditions using different irrigation systems. A field experiment was conducted using the commercial sugar beet variety Gazelle in sandy soil. Two doses (0 and 14 t ha−1) of GWC were applied to the soil. Three water deficiency levels (60, 80 and 100% of the soil field capacity) under either drip and sprinkler irrigation systems were applied. The application of 14 ton ha−1 of GWC resulted in the highest root and recoverable sugar yields, especially under the well-irrigated conditions under drip irrigation. Sugar beet root biofortification and juice quality were also significantly improved under drip irrigation in response to the application of 14 ton ha−1 of GWC by increasing sucrose content, quality index (Qz)% and recoverable sugar (RS)%. The application of GWC under drip irrigation enhanced water use efficiency for root (WUERY) and recoverable sugar yields (WUERSY), in particular under drip irrigation and water deficit conditions (60% of the soil field capacity). The soil physicochemical properties were significantly improved in response to the application of GWC. GWC application promoted the yield and biofortification of sugar beet by improving the soil physiochemical properties, and nutrient mobilization and uptake. The application of GWC is essential for sustainable sugar beet production and efficient irrigation water use in sandy soils.

Performance of AquaCrop model for predicting yield and biomass of okra (Abelmoschus esculentus) crop

The present study was carried out at Technology Park of College of Technology and Engineering, Udaipur, Rajasthan for two years (2019 and 2020) with 6 irrigation treatments and four replications in RBD. Among all the treatments, the treatment T2 (Irrigation at 85% field capacity of soil, based on soil moisture sensor based drip irrigation system) was found best in selected areas for growing okra [Abelmoschus esculentus (L.) Moench] crop under sensor based drip irrigation with maximum crop yield and biomass. In other water scarce regions of Rajasthan, the use of a simulation model can be a better option to predict the crop yield and biomass with respect to different irrigation levels. Therefore, calibration and validation of the AquaCrop model was done with the help of various data which were collected from field experiments during the study period (2019–20). The cut off temperature and base temperaturewere set as 32.5–100C, respectively. The model was successfully calibrated and validated with minimum prediction error, mean absolute error (MAE: 0.34–0.56), root mean square error (RMSE: 0.38–0.60) and maximum model efficiency (E ranges 0.8–0.95) and coefficient of correlation (R2 &gt; 0.9). The overall result indicate good performance of the model and can help in decision making as well as for irrigation water management for okra crop under the given conditions of Udaipur district of Rajasthan. It can be used to assess the influence of various environmental factors and management practices on okra crop growth.

Application of regression kriging and machine learning methods to estimate soil moisture constants in a semi-arid terrestrial area

In the current study, the use of regression-kriging (RK), artificial neural networks (ANN), support vector machines (SVM), and random forest (RF) methods from machine learning algorithms, were used to estimate field capacity (FC), permanent wilting point (PWP), available water content (AWC) and their performance was compared. A data set obtained from 354 surface soil samples taken randomly, mostly from agricultural areas is used. The soil data set includes pH, EC, calcium carbonate equivalent (CaCO3 equivalent), particle size distribution, and bulk density (BD) values. The results showed that while FC showed a negative strong correlation (p < 0.001) with sand (r:-0.69), BD (r:-0.85), and silt (r:-0.47), it showed a positive strong correlation (p < 0.001) with C (r: 0.90). Similarly, PWP showed a negative strong correlation with (p < 0.001) sand (r:-0.73), BD (r:-0.88), and silt (r:-0.42) but a positive strong correlation (p < 0.001) with C (r: 0.90). While AWC showed a negative strong correlation (p < 0.001) with sand (r:-0.61), BD (r:-0.76), it found a positive strong correlation (p < 0.001) with FC (r: 0.97), clay (r: 0.83), and PWP (r: 0.74). In the stepwise regression results showed that particle size were prominent as the most important factor in the regression equation created for FC, PWP and AWC. Moreover, FC is the most important factor to predict AWC. For the soil FC, ANN was best with excellent accuracy (RPD = 2.71), followed by SVM (2.42), RF (2.21) while RK was poor accuracy (1.10 and 1.04). Similarly, among the machine learning algorithms (RF and SVM), ANN obtained superiority by producing lower RRMSE (7.84%), RMSE (2.83%), MAE (2.37%), MAPE (7.45%), with the largest Lin’s concordance correlation coefficient (LCCC) (0.961) compared to other methods. For PWP and AWC, ANN was the best algorithm with excellent and good accuracy RPD 3.17 and 1.95 respecively. In addition, other machine learning algorithms have been the same value range in terms of LCCC. Therefore, we recommend the ANN machine-learning algorithm is more favorable to predict FC, PWP and AWC than both RK and other machine learning methods.

Biochar derived from papermill factories improves soil physical and hydraulic properties in no-till cotton fields

Whether biochar produced as a by-product of energy generation from the papermill industry, and often disposed in landfills, can be gainfully applied to commercial croplands has not been investigated. The objective of this study was to investigate the physical and hydraulic properties of soils in commercial cotton fields managed as no-till systems following repeated applications of biochar generated as a waste of a papermill plant. Undisturbed cores and disturbed soil samples were collected from 0–5 and 5–10 cm layers from five commercial no-till fields in Mississippi, USA that received 6.7 Mg ha−1 year−1 biochar for 0, 2, 3, 5 or 10 years. A number of physical, hydraulic, and chemical properties of these samples were measured in the lab. The results showed that biochar reduced the degree of soil compactness and increased soil aggregation and structural stability index. The findings were particularly apparent for the 10 years of consecutive application, which increased soil aggregate stability by up to 67%, reduced bulk density from 1.40 to 1.26 g cm−3, and reduced degree of compactness from 73.2% to 62.8%. Biochar increased soil porosity but much of this increase (55%) occurred for small pores (< 0.5 μm) with little effect on storage pores (0.5–50 μm) or transmission pores (> 50 μm). Consequently, biochar increased soil field capacity by up to 26%, but PAW increased by only 17%. Biochar significantly increased soil physical quality index score in the 0–5 cm layer from 0.16 to 0.26 and the increase was positively correlated with the number of years of application. The results suggest biochar generated as a byproduct of papermill could be land-applied in real-world crop production systems to improve soil health as an alternative to disposal in landfills.Graphical

Adapting 3-PG foliar variables to deciduous trees in response to water restriction: poplar short rotation plantations under Mediterranean conditions

Abstract Poplar plantations growing in short rotation are a crucial biomass source of raw material for bioenergy and/or bioproducts, making an important contribution towards achieving a low-carbon bioeconomy. To optimize yield predictions of poplar plantations, this study aims to adapt the foliar variables of the process-based model 3-PG (Physiological Principles Predicting Growth) to a deciduous species like poplar. A total of 138 trees were sampled from a poplar plantation of the highly productive hybrid P. x canadensis (‘AF2’) over a first rotation at a planting density of 10 000 trees ha−1. Two irrigation scenarios, full soil field capacity (FC) and 50% FC, were considered to take into account the impact of climate change in the context of irrigation restrictions. Based on this information, the objectives were not only to determine the required species-specific foliar parameters but also to adapt the 3-PG model architecture to a pattern of variation along each growing season and identify the foliar parameters which present a significant response to restrictive irrigation. For this, specific leaf area (SLA) changes were modelled and the litterfall rate (${\gamma}_F$), and maximum canopy quantum efficiency (${\alpha}_{Cx}$) were calculated. SLA follows a similar dynamic in terms of water availability and year, with SLA for mature leaves being 19.9 m2 kg−2 and the SLA at the beginning of the growing season 10.4 m2 kg2. Leaf litter season begins in late August and lasts until early December, with 26 per cent litterfall by October. Additionally, the highly sensitive parameter ${\alpha}_{Cx}$ was calibrated and a proposed value of 0.093 molC mol PAR−1 was used. The validation of the proposed parameterization showed realistic estimates of the changes of leaf biomass and LAI during the growing season. These results will enable improved 3-PG-based estimations of the real variation along the growing season of variables such as Net Primary Productivity, leaf litterfall or analysis of the soil–plant nutrient cycle.