Modeling water flow and phosphorus sorption in a soil amended with sewage sludge and olive pomace as compost or biochar
- Filipović, Vilim, Černe, Marko, Šimůnek, Jiří, Filipović, Lana, Romić, Marija, Ondrašek, Gabrijel, Bogunović, Igor, Mustać, Ivan, Krevh, Vedran, Ferenčević, Anja, Robinson, David, Palčić, Igor, Pasković, Igor, Goreta Ban, Smiljana, Užila, Zoran, Ban, Dean
- Authors: Filipović, Vilim , Černe, Marko , Šimůnek, Jiří , Filipović, Lana , Romić, Marija , Ondrašek, Gabrijel , Bogunović, Igor , Mustać, Ivan , Krevh, Vedran , Ferenčević, Anja , Robinson, David , Palčić, Igor , Pasković, Igor , Goreta Ban, Smiljana , Užila, Zoran , Ban, Dean
- Date: 2020
- Type: Text , Journal article
- Relation: Agronomy (Basel) Vol. 10, no. 8 (2020), p. 1163
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- Description: Organic amendments are often reported to improve soil properties, promote plant growth, and improve crop yield. This study aimed to investigate the effects of the biochar and compost produced from sewage sludge and olive pomace on soil hydraulic properties, water flow, and P transport (i.e., sorption) using numerical modeling (HYDRUS-1D) applied to two soil types (Terra Rosa and Rendzina). Evaporation and leaching experiments on soil cores and repacked soil columns were performed to determine the soil water retention, hydraulic conductivity, P leaching potential, and P sorption capacity of these mixtures. In the majority of treatments, the soil water retention showed a small increase compared to the control soil. A reliable fit with the modified van Genuchten model was found, which was also confirmed by water flow modeling of leaching experiments (R2 0.99). The results showed a high P sorption in all the treatments (Kd 21.24 to 53.68 cm3 g−1), and a high model reliability when the inverse modeling procedure was used (R2 0.93–0.99). Overall, adding sewage sludge or olive pomace as compost or biochar improved the Terra Rosa and Rendzina water retention and did not increase the P mobility in these soils, proving to be a sustainable source of carbon and P-rich materials.
- Authors: Filipović, Vilim , Černe, Marko , Šimůnek, Jiří , Filipović, Lana , Romić, Marija , Ondrašek, Gabrijel , Bogunović, Igor , Mustać, Ivan , Krevh, Vedran , Ferenčević, Anja , Robinson, David , Palčić, Igor , Pasković, Igor , Goreta Ban, Smiljana , Užila, Zoran , Ban, Dean
- Date: 2020
- Type: Text , Journal article
- Relation: Agronomy (Basel) Vol. 10, no. 8 (2020), p. 1163
- Full Text:
- Reviewed:
- Description: Organic amendments are often reported to improve soil properties, promote plant growth, and improve crop yield. This study aimed to investigate the effects of the biochar and compost produced from sewage sludge and olive pomace on soil hydraulic properties, water flow, and P transport (i.e., sorption) using numerical modeling (HYDRUS-1D) applied to two soil types (Terra Rosa and Rendzina). Evaporation and leaching experiments on soil cores and repacked soil columns were performed to determine the soil water retention, hydraulic conductivity, P leaching potential, and P sorption capacity of these mixtures. In the majority of treatments, the soil water retention showed a small increase compared to the control soil. A reliable fit with the modified van Genuchten model was found, which was also confirmed by water flow modeling of leaching experiments (R2 0.99). The results showed a high P sorption in all the treatments (Kd 21.24 to 53.68 cm3 g−1), and a high model reliability when the inverse modeling procedure was used (R2 0.93–0.99). Overall, adding sewage sludge or olive pomace as compost or biochar improved the Terra Rosa and Rendzina water retention and did not increase the P mobility in these soils, proving to be a sustainable source of carbon and P-rich materials.
Land management impacts on soil properties and initial soil erosion processes in olives and vegetable crops
- Bogunovic, Igor, Telak, Leon Josip, Pereira, Paulo, Filipovic, Vilim, Filipovic, Lana, Percin, Aleksandra, Durdevic, Boris, Birkás, Márta, Dekemati, Igor, Comino, Jesus Rodrigo
- Authors: Bogunovic, Igor , Telak, Leon Josip , Pereira, Paulo , Filipovic, Vilim , Filipovic, Lana , Percin, Aleksandra , Durdevic, Boris , Birkás, Márta , Dekemati, Igor , Comino, Jesus Rodrigo
- Date: 2020
- Type: Text , Journal article
- Relation: Journal of Hydrology and Hydromechanics Vol. 68, no. 4 (2020), p. 328-337
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- Description: This research aims to assess the impacts of soil use management on runoff, soil losses, and their main soil controls in vegetable cropland (CROP), tilled olives (OT), and grass-covered olive orchards (OGC) on Leptosol in Croatia. Soil analysis and rainfall simulation experiments were conducted to quantify runoff (Run), soil, and nutrient losses. Bulk density (BD) was significantly higher at OT plots, in addition to the CROP plots. Water-stable aggregates (WSA), mean weight diameter (MWD), and soil organic matter (OM) were significantly higher in OGC plots compared to the other land uses. Run and soil loss (SL) were significantly higher in CROP and OT plots compared to the OGC plots. The CROP plots showed soil management that can be considered as unsustainable with 52, 68- and 146-times higher losses of phosphorus (P loss), nitrogen (N loss), and carbon (C loss) compared to the OGC plots. The principal component analysis showed that MWD was associated with vegetation cover (VC), water-holding capacity (WHC), WSA, OM, total nitrogen (TN), time to ponding (TP), and time to runoff (TR). These variables were negatively related to P2O5, Run, SL, and P, N, and C loss. Results indicate the need for the adoption of conservation strategies in croplands and olive orchards.
- Authors: Bogunovic, Igor , Telak, Leon Josip , Pereira, Paulo , Filipovic, Vilim , Filipovic, Lana , Percin, Aleksandra , Durdevic, Boris , Birkás, Márta , Dekemati, Igor , Comino, Jesus Rodrigo
- Date: 2020
- Type: Text , Journal article
- Relation: Journal of Hydrology and Hydromechanics Vol. 68, no. 4 (2020), p. 328-337
- Full Text:
- Reviewed:
- Description: This research aims to assess the impacts of soil use management on runoff, soil losses, and their main soil controls in vegetable cropland (CROP), tilled olives (OT), and grass-covered olive orchards (OGC) on Leptosol in Croatia. Soil analysis and rainfall simulation experiments were conducted to quantify runoff (Run), soil, and nutrient losses. Bulk density (BD) was significantly higher at OT plots, in addition to the CROP plots. Water-stable aggregates (WSA), mean weight diameter (MWD), and soil organic matter (OM) were significantly higher in OGC plots compared to the other land uses. Run and soil loss (SL) were significantly higher in CROP and OT plots compared to the OGC plots. The CROP plots showed soil management that can be considered as unsustainable with 52, 68- and 146-times higher losses of phosphorus (P loss), nitrogen (N loss), and carbon (C loss) compared to the OGC plots. The principal component analysis showed that MWD was associated with vegetation cover (VC), water-holding capacity (WHC), WSA, OM, total nitrogen (TN), time to ponding (TP), and time to runoff (TR). These variables were negatively related to P2O5, Run, SL, and P, N, and C loss. Results indicate the need for the adoption of conservation strategies in croplands and olive orchards.
Global environmental changes impact soil hydraulic functions through biophysical feedbacks
- Robinson, David, Hopmans, Jan, Filipovic, Vilim, van der Ploeg, Martine, Lebron, Inma, Jones, Scott, Reinsch, Sabine, Jarvis, Nick, Tuller, Markus
- Authors: Robinson, David , Hopmans, Jan , Filipovic, Vilim , van der Ploeg, Martine , Lebron, Inma , Jones, Scott , Reinsch, Sabine , Jarvis, Nick , Tuller, Markus
- Date: 2019
- Type: Text , Journal article
- Relation: Global Change Biology Vol. 25, no. 6 (2019), p. 1895-1904
- Full Text: false
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- Description: Although only representing 0.05% of global freshwater, or 0.001% of all global water, soil water supports all terrestrial biological life. Soil moisture behaviour in most models is constrained by hydraulic parameters that do not change. Here we argue that biological feedbacks from plants, macro-fauna and the microbiome influence soil structure, and thus the soil hydraulic parameters and the soil water content signals we observe. Incorporating biological feedbacks into soil hydrological models is therefore important for understanding environmental change and its impacts on ecosystems. We anticipate that environmental change will accelerate and modify soil hydraulic function. Increasingly, we understand the vital role that soil moisture exerts on the carbon cycle and other environmental threats such as heatwaves, droughts and floods, wildfires, regional precipitation patterns, disease regulation and infrastructure stability, in addition to agricultural production. Biological feedbacks may result in changes to soil hydraulic function that could be irreversible, resulting in alternative stable states (ASS) of soil moisture. To explore this, we need models that consider all the major feedbacks between soil properties and soil-plant-faunal-microbial-atmospheric processes, which is something we currently do not have. Therefore, a new direction is required to incorporate a dynamic description of soil structure and hydraulic property evolution into soil-plant-atmosphere, or land surface, models that consider feedbacks from land use and climate drivers of change, so as to better model ecosystem dynamics.
- Braunack, Michael, Adhikari, Raju, Freischmidt, George, Johnston, Priscilla, Casey, Philip S., Wang, Yusong, Bristow, Keith, Filipović, Lana, Filipović, Vilim
- Authors: Braunack, Michael , Adhikari, Raju , Freischmidt, George , Johnston, Priscilla , Casey, Philip S. , Wang, Yusong , Bristow, Keith , Filipović, Lana , Filipović, Vilim
- Date: 2020
- Type: Text , Journal article
- Relation: Agronomy (Basel) Vol. 10, no. 4 (2020), p. 584
- Full Text: false
- Reviewed:
- Description: Preformed biodegradable and next generation sprayable biodegradable polymer membrane (SBPM) formulations, which biodegrade to non-harmful products (water, carbon dioxide and microbial biomass), have been introduced as an alternative to plastic mulch films in order to mitigate plastic pollution of the environment. In this preliminary field study on cotton (Gossypium hirsutum L.), a novel SBPM technology was compared to preformed slotted oxo-degradable plastic (ODP) mulch film and no mulch control (CON) in terms of yield, crop water productivity (CWP), and soil temperature. The first results showed higher CWP and crop yield, and increased soil water content under the SBPM cover. This study indicates that SBPM technology could perform at similar level as ODP or comparable films under field conditions and, at the same time, provide environmentally sustainable agricultural cropping practices. Additionally, the fully treated, non-replicated SBPM plot had a wetter soil profile throughout the entire crop season. This innovative technology has shown a high potential even at this early stage of development, indicating that advances in formulation and further testing can lead to significant improvements and thus increased use in crop production systems.
Comparative analysis of machine and deep learning models for soil properties prediction from hyperspectral visual band
- Datta, Dristi, Paul, Manoranjan, Murshed, Manzur, Teng, Shyh Wei, Schmidtke, Leigh
- Authors: Datta, Dristi , Paul, Manoranjan , Murshed, Manzur , Teng, Shyh Wei , Schmidtke, Leigh
- Date: 2023
- Type: Text , Journal article
- Relation: Environments Vol. 10, no. 5 (2023), p. 77
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- Description: Estimating various properties of soil, including moisture, carbon, and nitrogen, is crucial for studying their correlation with plant health and food production. However, conventional methods such as oven-drying and chemical analysis are laborious, expensive, and only feasible for a limited land area. With the advent of remote sensing technologies like multi/hyperspectral imaging, it is now possible to predict soil properties non-invasive and cost-effectively for a large expanse of bare land. Recent research shows the possibility of predicting those soil contents from a wide range of hyperspectral data using good prediction algorithms. However, these kinds of hyperspectral sensors are expensive and not widely available. Therefore, this paper investigates different machine and deep learning techniques to predict soil nutrient properties using only the red (R), green (G), and blue (B) bands data to propose a suitable machine/deep learning model that can be used as a rapid soil test. Another objective of this research is to observe and compare the prediction accuracy in three cases i. hyperspectral band ii. full spectrum of the visual band, and iii. three-channel of RGB band and provide a guideline to the user on which spectrum information they should use to predict those soil properties. The outcome of this research helps to develop a mobile application that is easy to use for a quick soil test. This research also explores learning-based algorithms with significant feature combinations and their performance comparisons in predicting soil properties from visual band data. For this, we also explore the impact of dimensional reduction (i.e., principal component analysis) and transformations (i.e., empirical mode decomposition) of features. The results show that the proposed model can comparably predict the soil contents from the three-channel RGB data.
- Authors: Datta, Dristi , Paul, Manoranjan , Murshed, Manzur , Teng, Shyh Wei , Schmidtke, Leigh
- Date: 2023
- Type: Text , Journal article
- Relation: Environments Vol. 10, no. 5 (2023), p. 77
- Full Text:
- Reviewed:
- Description: Estimating various properties of soil, including moisture, carbon, and nitrogen, is crucial for studying their correlation with plant health and food production. However, conventional methods such as oven-drying and chemical analysis are laborious, expensive, and only feasible for a limited land area. With the advent of remote sensing technologies like multi/hyperspectral imaging, it is now possible to predict soil properties non-invasive and cost-effectively for a large expanse of bare land. Recent research shows the possibility of predicting those soil contents from a wide range of hyperspectral data using good prediction algorithms. However, these kinds of hyperspectral sensors are expensive and not widely available. Therefore, this paper investigates different machine and deep learning techniques to predict soil nutrient properties using only the red (R), green (G), and blue (B) bands data to propose a suitable machine/deep learning model that can be used as a rapid soil test. Another objective of this research is to observe and compare the prediction accuracy in three cases i. hyperspectral band ii. full spectrum of the visual band, and iii. three-channel of RGB band and provide a guideline to the user on which spectrum information they should use to predict those soil properties. The outcome of this research helps to develop a mobile application that is easy to use for a quick soil test. This research also explores learning-based algorithms with significant feature combinations and their performance comparisons in predicting soil properties from visual band data. For this, we also explore the impact of dimensional reduction (i.e., principal component analysis) and transformations (i.e., empirical mode decomposition) of features. The results show that the proposed model can comparably predict the soil contents from the three-channel RGB data.
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