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The knowledge about nutrient dynamics in the soil is pivotal for sustainable agriculture. A comprehensive research trial can retort unanswered questions. Dynamics of nutrients sourced from organic amendment types (chicken manure, dairy manure, and MilorganiteTM) applied at different rates (0, 168, 336, 672 kg total N/ha) were monitored within and below the rootzone of collard greens cultivated on a sandy loam soil in Prairie View, TX, USA. Macro- and micronutrients (e.g., TN: total nitrogen, P: phosphorous, K: potassium, Na: sodium, Ca: calcium, Mg: magnesium, B: boron, Cu: copper, Fe: iron, and Zn: zinc) were analyzed from soil solution samples collected during six sampling periods from within and below the rootzone. As hypothesized, the organic amendment types and rates significantly (p< 0.05 and/or 0.01) affected nutrient dynamics within and below the crop rootzone. Chicken manure released significantly more TN, P, K, Na, Ca, Mg, B, Cu, and Fe than the other two amendments. The application of chicken manure and MilorganiteTM resulted in higher below-the-rootzone leachate concentration of TN, Na, Mg, and Ca than in the leachates of dairy manure. Dairy manure treatments had the lowest concentrations of TN, Ca, and Mg; whereas, MilorganiteTM had the lowest concentrations of P, K, Na, B, and Cu in the collected leachates. The higher level of P (i.e., 4% in MilorganiteTM as compared to 2 and 0.5% in chicken and dairy manures, respectively, might have reduced the formation of Vesicular-Arbuscular (VA) mycorrhizae—a fungus with the ability to dissolve the soil P, resulting in slow release of P from MilorganiteTM treatment than from the other two treatments. Patterns of nutrient dynamics varied with rain and irrigation events under the effects of the soil water and time lapse of the amendment applications’ rates and types. All the macronutrients were present within the rootzone and leached below the rootzone, except Na. The dynamic of nutrients was element-specific and was influenced by the amendments’ type and application rate.
Ripendra Awal; Almoutaz Hassan; Farhat Abbas; Ali Fares; Haimanote Bayabil; Ram Ray; Selamawit Woldesenbet. Patterns of Nutrient Dynamics within and below the Rootzone of Collard Greens Grown under Different Organic Amendment Types and Rates. Sustainability 2021, 13, 6857 .
AMA StyleRipendra Awal, Almoutaz Hassan, Farhat Abbas, Ali Fares, Haimanote Bayabil, Ram Ray, Selamawit Woldesenbet. Patterns of Nutrient Dynamics within and below the Rootzone of Collard Greens Grown under Different Organic Amendment Types and Rates. Sustainability. 2021; 13 (12):6857.
Chicago/Turabian StyleRipendra Awal; Almoutaz Hassan; Farhat Abbas; Ali Fares; Haimanote Bayabil; Ram Ray; Selamawit Woldesenbet. 2021. "Patterns of Nutrient Dynamics within and below the Rootzone of Collard Greens Grown under Different Organic Amendment Types and Rates." Sustainability 13, no. 12: 6857.
Phytoremediation is a cost-effective and environmentally friendly approach that can be used for the remediation of metals in polluted soil. This study used a hedge plant–calico (Alternanthera bettzickiana (Regel) G. Nicholson) to determine the role of citric acid in lead (Pb) phytoremediation by exposing it to different concentrations of Pb (0, 200, 500, and 1000 mg kg−1) as well as in a combination with citric acid concentration (0, 250, 500 µM). The analysis of variance was applied on results for significant effects of the independent variables on the dependent variables using SPSS (ver10). According to the results, maximum Pb concentration was measured in the upper parts of the plant. An increase in dry weight biomass, plant growth parameters, and photosynthetic contents was observed with the increase of Pb application (200 mg kg−1) in soil while a reduced growth was experienced at higher Pb concentration (1000 mg kg−1). The antioxidant enzymatic activities like superoxide dismutase (SOD) and peroxidase (POD) were enhanced under lower Pb concentration (200, 500 mg kg−1), whereas the reduction occurred at greater metal concentration Pb (1000 mg kg−1). There was a usual reduction in electrolyte leakage (EL) at lower Pb concentration (200, 500 mg kg−1), whereas EL increased at maximum Pb concentration (1000 mg kg−1). We concluded that this hedge plant, A. Bettzickiana, has the greater ability to remediate polluted soils aided with citric acid application.
Urooj Kanwal; Muhammad Ibrahim; Farhat Abbas; Muhammad Yamin; Fariha Jabeen; Anam Shahzadi; Aitazaz Farooque; Muhammad Imtiaz; Allah Ditta; Shafaqat Ali. Phytoextraction of Lead Using a Hedge Plant [Alternanthera bettzickiana (Regel) G. Nicholson]: Physiological and Biochemical Alterations through Bioresource Management. Sustainability 2021, 13, 5074 .
AMA StyleUrooj Kanwal, Muhammad Ibrahim, Farhat Abbas, Muhammad Yamin, Fariha Jabeen, Anam Shahzadi, Aitazaz Farooque, Muhammad Imtiaz, Allah Ditta, Shafaqat Ali. Phytoextraction of Lead Using a Hedge Plant [Alternanthera bettzickiana (Regel) G. Nicholson]: Physiological and Biochemical Alterations through Bioresource Management. Sustainability. 2021; 13 (9):5074.
Chicago/Turabian StyleUrooj Kanwal; Muhammad Ibrahim; Farhat Abbas; Muhammad Yamin; Fariha Jabeen; Anam Shahzadi; Aitazaz Farooque; Muhammad Imtiaz; Allah Ditta; Shafaqat Ali. 2021. "Phytoextraction of Lead Using a Hedge Plant [Alternanthera bettzickiana (Regel) G. Nicholson]: Physiological and Biochemical Alterations through Bioresource Management." Sustainability 13, no. 9: 5074.
Climate change is impacting different parts of Canada in a diverse manner. Impacts on temperature, precipitation, and stream flows have been reviewed and discussed region and province-wise. The average warming in Canada was 1.6 °C during the 20th century, which is 0.6 °C above the global average. Spatially, southern and western parts got warmer than others, and temporally winters got warmer than summers. Explicit implications include loss of Arctic ice @ 12.8% per decade, retreat of British Columbian glaciers @ 40–70 giga-tons/year, and sea level rise of 32 cm/20th century on the east coast, etc. The average precipitation increased since 1950s from under 500 to around 600 mm/year, with up to a 10% reduction in Prairies and up to a 35% increase in northern and southern parts. Precipitation patterns exhibited short-intense trends, due to which urban drainage and other hydraulic structures may require re-designing. Streamflow patterns exhibited stability overall with a temporal re-distribution and intense peaks. However, surface water withdrawals were well under sustainable limits. For agriculture, the rainfed and semi-arid regions may require supplemental irrigation during summers. Availability of water is mostly not a limitation, but the raised energy demands thereof are. Supplemental irrigation by water and energy-efficient systems, adaptation, and regulation can ensure sustainability under the changing climate.
Ahmad Bhatti; Aitazaz Farooque; Nicholas Krouglicof; Qing Li; Wayne Peters; Farhat Abbas; Bishnu Acharya. An Overview of Climate Change Induced Hydrological Variations in Canada for Irrigation Strategies. Sustainability 2021, 13, 4833 .
AMA StyleAhmad Bhatti, Aitazaz Farooque, Nicholas Krouglicof, Qing Li, Wayne Peters, Farhat Abbas, Bishnu Acharya. An Overview of Climate Change Induced Hydrological Variations in Canada for Irrigation Strategies. Sustainability. 2021; 13 (9):4833.
Chicago/Turabian StyleAhmad Bhatti; Aitazaz Farooque; Nicholas Krouglicof; Qing Li; Wayne Peters; Farhat Abbas; Bishnu Acharya. 2021. "An Overview of Climate Change Induced Hydrological Variations in Canada for Irrigation Strategies." Sustainability 13, no. 9: 4833.
Heat stress provokes thermal discomfort to people living in semiarid and arid climates. This study evaluates thermal discomfort levels, building design concepts, and some heat mitigation strategies in low-income neighborhoods of Faisalabad, Pakistan. The outdoor and indoor weather data are collected from April to August 2016 using a weather station installed ad hoc in urban settings, and the 52 houses of the five low-income participating communities living in congested and less environment-friendly areas of Faisalabad. The discomfort index values, related to the building design concepts, including (i) house orientation to sunlight and (ii) house ventilation, are calculated from outdoor and indoor dry-bulb and wet-bulb temperatures. Our results show that although June was the hottest month of summer 2016, based on the monthly mean temperature of the Faisalabad region, the month of May produced the highest discomfort levels, which were higher in houses exposed to sunlight and without ventilation. The study also identifies some popular heat mitigation strategies adopted by the five participating low-income communities during various heat-related health complaints. The strategies are gender-biased and have medical, cultural/customary backgrounds. For example, about 52% of the males and 28% of the females drank more water during dehydration, diarrhea, and eye infection. Over 11% and 19% of the males and females, respectively, moved to cooler places during fever. About 43% of the males and 51% of the females took water showers and rested to combat flu (runny nose), headache, and nosebleed. The people did not know how to cure muscular fatigue, skin allergy (from a type of Milia), and mild temperature. Planting trees in an area and developing open parks with greenery and thick canopy trees can be beneficial for neighborhoods resembling those evaluated in this study.
Sana Ehsan; Farhat Abbas; Muhammad Ibrahim; Bashir Ahmad; Aitazaz Farooque. Thermal Discomfort Levels, Building Design Concepts, and Some Heat Mitigation Strategies in Low-Income Communities of a South Asian City. International Journal of Environmental Research and Public Health 2021, 18, 2535 .
AMA StyleSana Ehsan, Farhat Abbas, Muhammad Ibrahim, Bashir Ahmad, Aitazaz Farooque. Thermal Discomfort Levels, Building Design Concepts, and Some Heat Mitigation Strategies in Low-Income Communities of a South Asian City. International Journal of Environmental Research and Public Health. 2021; 18 (5):2535.
Chicago/Turabian StyleSana Ehsan; Farhat Abbas; Muhammad Ibrahim; Bashir Ahmad; Aitazaz Farooque. 2021. "Thermal Discomfort Levels, Building Design Concepts, and Some Heat Mitigation Strategies in Low-Income Communities of a South Asian City." International Journal of Environmental Research and Public Health 18, no. 5: 2535.
Biochar produced from transforming bioresource waste can benefit sustainable agriculture and support circular bioeconomy. The objective of this study was to evaluate the effect of the application of biochar, produced from wheat straws, and a nitrification inhibitor, sourced from neem (Azadirachta indica), in combinition with the recommended synthetic fertilizer on soil properties, maize (Zea mays L.) plant growth characteristics, and maize grain yield and quality paramters. The nitrification inhibitor was used with the concentrations of 5 and 10 mL pot−1 (N1 and N2, respectively) with four levels of biochar (B0 = 0 g, B1 = 35 g, B2 = 70 g, B3 = 105 g, B4 = 140 g pot−1), one recommended nitrogen, phosphorous, and potassium syntactic fertilizer (250, 125, and 100 kg ha−1, respectively) treatment, and one control treatment. The results showed that the nitrification inhibitor enhanced crop growth while the application of biochar significantly improved soil fertility. The application of biochar significantly enhanced soil organic matter and soil nitrogen as compared with nitrogen–phosphorus–potassium treatment. The highest root length (65.43 cm) and root weight (50.25 g) were observed in the maize plants treated with B4 and N2 combinedly. The grain yield, total biomass production, protein content from biochar’s B4, and nitrogen–phosphorus–potassium treatments were not significantly different from each other. The application of 140 g biochar pot−1 (B4) with nitrification inhibitor (10 mL pot−1) resulted in higher crop yield and the highest protein contents in maize grains as compared to the control treatments. Therefore, the potential of biochar application in combination with nitrification inhibitor may be used as the best nutrient management practice after verifying these findings at a large-scale field study. Based on the experimental findings, the applied potential of the study treatments, and results of economic analysis, it can be said that biochar has an important role to play in the circular bioeconomy.
Farhat Abbas; Hafiz Hammad; Farhat Anwar; Aitazaz Farooque; Rashid Jawad; Hafiz Bakhat; Muhammad Naeem; Sajjad Ahmad; Saeed Qaisrani. Transforming a Valuable Bioresource to Biochar, Its Environmental Importance, and Potential Applications in Boosting Circular Bioeconomy While Promoting Sustainable Agriculture. Sustainability 2021, 13, 2599 .
AMA StyleFarhat Abbas, Hafiz Hammad, Farhat Anwar, Aitazaz Farooque, Rashid Jawad, Hafiz Bakhat, Muhammad Naeem, Sajjad Ahmad, Saeed Qaisrani. Transforming a Valuable Bioresource to Biochar, Its Environmental Importance, and Potential Applications in Boosting Circular Bioeconomy While Promoting Sustainable Agriculture. Sustainability. 2021; 13 (5):2599.
Chicago/Turabian StyleFarhat Abbas; Hafiz Hammad; Farhat Anwar; Aitazaz Farooque; Rashid Jawad; Hafiz Bakhat; Muhammad Naeem; Sajjad Ahmad; Saeed Qaisrani. 2021. "Transforming a Valuable Bioresource to Biochar, Its Environmental Importance, and Potential Applications in Boosting Circular Bioeconomy While Promoting Sustainable Agriculture." Sustainability 13, no. 5: 2599.
This study evaluated the potential of using machine vision in combination with deep learning (DL) to identify the early blight disease in real-time for potato production systems. Four fields were selected to collect images (n = 5199) of healthy and diseased potato plants under variable lights and shadow effects. A database was constructed using DL to identify the disease infestation at different stages throughout the growing season. Three convolutional neural networks (CNNs), namely GoogleNet, VGGNet, and EfficientNet, were trained using the PyTorch framework. The disease images were classified into three classes (2-class, 4-class, and 6-class) for accurate disease identification at different growth stages. Results of 2-class CNNs for disease identification revealed the significantly better performance of EfficientNet and VGGNet when compared with the GoogleNet (FScore range: 0.84–0.98). Results of 4-Class CNNs indicated better performance of EfficientNet when compared with other CNNs (FScore range: 0.79–0.94). Results of 6-class CNNs showed similar results as 4-class, with EfficientNet performing the best. GoogleNet, VGGNet, and EfficientNet inference time values ranged from 6.8–8.3, 2.1–2.5, 5.95–6.53 frames per second, respectively, on a Dell Latitude 5580 using graphical processing unit (GPU) mode. Overall, the CNNs and DL frameworks used in this study accurately classified the early blight disease at different stages. Site-specific application of fungicides by accurately identifying the early blight infected plants has a strong potential to reduce agrochemicals use, improve the profitability of potato growers, and lower environmental risks (runoff of fungicides to water bodies).
Hassan Afzaal; Aitazaz Farooque; Arnold Schumann; Nazar Hussain; Andrew McKenzie-Gopsill; Travis Esau; Farhat Abbas; Bishnu Acharya. Detection of a Potato Disease (Early Blight) Using Artificial Intelligence. Remote Sensing 2021, 13, 411 .
AMA StyleHassan Afzaal, Aitazaz Farooque, Arnold Schumann, Nazar Hussain, Andrew McKenzie-Gopsill, Travis Esau, Farhat Abbas, Bishnu Acharya. Detection of a Potato Disease (Early Blight) Using Artificial Intelligence. Remote Sensing. 2021; 13 (3):411.
Chicago/Turabian StyleHassan Afzaal; Aitazaz Farooque; Arnold Schumann; Nazar Hussain; Andrew McKenzie-Gopsill; Travis Esau; Farhat Abbas; Bishnu Acharya. 2021. "Detection of a Potato Disease (Early Blight) Using Artificial Intelligence." Remote Sensing 13, no. 3: 411.
Growing Brassica rapa L. (Brassica rapa subsp. campestris (Linn.) Clapham) with wastewater and their use as a fodder for animals is a common practice in suburb of all cities in Punjab, Pakistan, despite the wastewater containing heavy metals is of public health concern. This study assessed the risk of heavy metals on animal health via consumption of B. rapa as fodder grown with wastewater, tube-well and canal water, and its source apportionment, in suburb of Multan City, Pakistan. Samples of B. rapa (n = 30) were collected from six agricultural farms and analyzed for cadmium (Cd), chromium (Cr), copper (Cu), manganese (Mn), nickel (Ni), and lead (Pb) by inductively coupled plasma-optical emission spectrometry (ICP-OES). Total target health quotient (TTHQ) values ranged 47.22 to 136.64 in wastewater irrigation farm, 2.32 to 3.71 in canal water, and 4.86 to 7.50 in tube-well water irrigation farms, respectively exhibiting high carcinogenic health risk to animals across the farms. B. rapa grown with industrial effluents exhibited the highest carcinogenic health risk, while the canal water posed the lowest risk. Multivariate statistical analyses indicated that the wastewater samples containing heavy metals and contaminated soils were common sources of B. rapa contamination. Proper treatment of wastewater for removal of toxic elements before application in agricultural fields may safeguard the health of animals, public, and the ecosystem.
Zafar Iqbal; Farhat Abbas; Muhammad Ibrahim; Tahir Imran Qureshi; Matin Gul; Abid Mahmood. Assessment of heavy metal pollution in Brassica plants and their impact on animal health in Punjab, Pakistan. Environmental Science and Pollution Research 2021, 28, 22768 -22778.
AMA StyleZafar Iqbal, Farhat Abbas, Muhammad Ibrahim, Tahir Imran Qureshi, Matin Gul, Abid Mahmood. Assessment of heavy metal pollution in Brassica plants and their impact on animal health in Punjab, Pakistan. Environmental Science and Pollution Research. 2021; 28 (18):22768-22778.
Chicago/Turabian StyleZafar Iqbal; Farhat Abbas; Muhammad Ibrahim; Tahir Imran Qureshi; Matin Gul; Abid Mahmood. 2021. "Assessment of heavy metal pollution in Brassica plants and their impact on animal health in Punjab, Pakistan." Environmental Science and Pollution Research 28, no. 18: 22768-22778.
Cannabis (Cannabis sativa L.) growers worldwide lack reliable and research-based information about precision management practices (PMP) of cannabis. The history, legal framework, and PMP for cultivation of cannabis have been reviewed with special emphasis on water management, nutrient management, and disease control for optimum cannabis production. The aim is to provide guidelines for precision farming of cannabis to meet fibrous and medicinal needs of the humankind. Therefore, the scope of this chapter is for the potential of hemp cultivation to meet industry needs of fiber and medicine. Methods of irrigation scheduling, nutrient applications, and keeping greenhouse hygienically clean for disease-free (i.e., powdery mildew) hemp production are discussed. Reviewed and recommended application rates of irrigation and nutrients, and environment controls have been tabulated. Chemical, biological, and physical controls of PM control and crop input requirements for disease-free cultivation of hemp are presented.
Aitazaz Ahsan Farooque; Farhat Abbas. Precision Management Practices for Legal Cultivation of Cannabis (Cannabis sativa L.). Advances in Environmental Engineering and Green Technologies 2021, 187 -209.
AMA StyleAitazaz Ahsan Farooque, Farhat Abbas. Precision Management Practices for Legal Cultivation of Cannabis (Cannabis sativa L.). Advances in Environmental Engineering and Green Technologies. 2021; ():187-209.
Chicago/Turabian StyleAitazaz Ahsan Farooque; Farhat Abbas. 2021. "Precision Management Practices for Legal Cultivation of Cannabis (Cannabis sativa L.)." Advances in Environmental Engineering and Green Technologies , no. : 187-209.
The uniform application (UA) of agrochemicals results in the over-application of harmful chemicals, increases crop input costs, and deteriorates the environment when compared with variable rate application (VA). A smart variable rate sprayer (SVRS) was designed, developed, and tested using deep learning (DL) for VA application of agrochemicals. Real-time testing of the SVRS took place for detecting and spraying and/or skipping lambsquarters weed and early blight infected and healthy potato plants. About 24,000 images were collected from potato fields in Prince Edward Island and New Brunswick under varying sunny, cloudy, and partly cloudy conditions and processed/trained using YOLOv3 and tiny-YOLOv3 models. Due to faster performance, the tiny-YOLOv3 was chosen to deploy in SVRS. A laboratory experiment was designed under factorial arrangements, where the two spraying techniques (UA and VA) and the three weather conditions (cloudy, partly cloudy, and sunny) were the two independent variables with spray volume consumption as a response variable. The experimental treatments had six repetitions in a 2 × 3 factorial design. Results of the two-way ANOVA showed a significant effect of spraying application techniques on volume consumption of spraying liquid (p-value < 0.05). There was no significant effect of weather conditions and interactions between the two independent variables on volume consumption during weeds and simulated diseased plant detection experiments (p-value > 0.05). The SVRS was able to save 42 and 43% spraying liquid during weeds and simulated diseased plant detection experiments, respectively. Water sensitive papers’ analysis showed the applicability of SVRS for VA with >40% savings of spraying liquid by SVRS when compared with UA. Field applications of this technique would reduce the crop input costs and the environmental risks in conditions (weed and disease) like experimental testing.
Nazar Hussain; Aitazaz Farooque; Arnold Schumann; Andrew McKenzie-Gopsill; Travis Esau; Farhat Abbas; Bishnu Acharya; Qamar Zaman. Design and Development of a Smart Variable Rate Sprayer Using Deep Learning. Remote Sensing 2020, 12, 4091 .
AMA StyleNazar Hussain, Aitazaz Farooque, Arnold Schumann, Andrew McKenzie-Gopsill, Travis Esau, Farhat Abbas, Bishnu Acharya, Qamar Zaman. Design and Development of a Smart Variable Rate Sprayer Using Deep Learning. Remote Sensing. 2020; 12 (24):4091.
Chicago/Turabian StyleNazar Hussain; Aitazaz Farooque; Arnold Schumann; Andrew McKenzie-Gopsill; Travis Esau; Farhat Abbas; Bishnu Acharya; Qamar Zaman. 2020. "Design and Development of a Smart Variable Rate Sprayer Using Deep Learning." Remote Sensing 12, no. 24: 4091.
The delineation of management zones (MZs) has been suggested as a solution to mitigate adverse impacts of soil variability on potato tuber yield. This study quantified the spatial patterns of variability in soil and crop properties to delineate MZs for site-specific soil fertility characterization of potato fields through proximal sensing of fields. Grid sampling strategy was adopted to collect soil and crop data from two potato fields in Prince Edward Island (PEI). DUALEM-2 sensor, Time Domain Reflectometry (TDR-300), GreenSeeker were used to collect soil ground conductivity parameter horizontal coplanar geometry (HCP), soil moisture content (θ), and normalized difference vegetative index (NDVI), respectively. Soil organic matter (SOM), soil pH, phosphorous (P), potash (K), iron (Fe), lime index (LI), and cation exchange capacity (CEC) were determined from soil samples collected from each grid. Stepwise regression shortlisted the major properties of soil and crop that explained 71 to 86% of within-field variability. The cluster analysis grouped the soil and crop data into three zones, termed as excellent, medium, and poor at a 40% similarity level. The coefficient of variation and the interpolated maps characterized least to moderate variability of soil fertility parameters, except for HCP and K that were highly variable. The results of multiple means comparison indicated that the tuber yield and HCP were significantly different in all MZs. The significant relationship between HCP and yield suggested that the ground conductivity data could be used to develop MZs for site-specific fertilization in potato fields similar to those used in this study.
Humna Khan; Aitazaz A. Farooque; Bishnu Acharya; Farhat Abbas; Travis J. Esau; Qamar U. Zaman. Delineation of Management Zones for Site-Specific Information about Soil Fertility Characteristics through Proximal Sensing of Potato Fields. Agronomy 2020, 10, 1854 .
AMA StyleHumna Khan, Aitazaz A. Farooque, Bishnu Acharya, Farhat Abbas, Travis J. Esau, Qamar U. Zaman. Delineation of Management Zones for Site-Specific Information about Soil Fertility Characteristics through Proximal Sensing of Potato Fields. Agronomy. 2020; 10 (12):1854.
Chicago/Turabian StyleHumna Khan; Aitazaz A. Farooque; Bishnu Acharya; Farhat Abbas; Travis J. Esau; Qamar U. Zaman. 2020. "Delineation of Management Zones for Site-Specific Information about Soil Fertility Characteristics through Proximal Sensing of Potato Fields." Agronomy 10, no. 12: 1854.
The original version of this paper was published with error. Corresponding author requested to make a necessary correction in the spelling for the last author. The correct name is “Travis Esau” instead of “Travis Easu”.
Aitazaz A. Farooque; Qamar Zaman; Farhat Abbas; Hafiz Mohkum Hammad; Bishnu Acharya; Travis Esau. Correction to: How can potatoes be smartly cultivated with biochar as a soil nutrient amendment technique in Atlantic Canada? Arabian Journal of Geosciences 2020, 13, 1 -1.
AMA StyleAitazaz A. Farooque, Qamar Zaman, Farhat Abbas, Hafiz Mohkum Hammad, Bishnu Acharya, Travis Esau. Correction to: How can potatoes be smartly cultivated with biochar as a soil nutrient amendment technique in Atlantic Canada? Arabian Journal of Geosciences. 2020; 13 (18):1-1.
Chicago/Turabian StyleAitazaz A. Farooque; Qamar Zaman; Farhat Abbas; Hafiz Mohkum Hammad; Bishnu Acharya; Travis Esau. 2020. "Correction to: How can potatoes be smartly cultivated with biochar as a soil nutrient amendment technique in Atlantic Canada?" Arabian Journal of Geosciences 13, no. 18: 1-1.
Proximal sensing techniques can potentially survey soil and crop variables responsible for variations in crop yield. The full potential of these precision agriculture technologies may be exploited in combination with innovative methods of data processing such as machine learning (ML) algorithms for the extraction of useful information responsible for controlling crop yield. Four ML algorithms, namely linear regression (LR), elastic net (EN), k-nearest neighbor (k-NN), and support vector regression (SVR), were used to predict potato (Solanum tuberosum) tuber yield from data of soil and crop properties collected through proximal sensing. Six fields in Atlantic Canada including three fields in Prince Edward Island (PE) and three fields in New Brunswick (NB) were sampled, over two (2017 and 2018) growing seasons, for soil electrical conductivity, soil moisture content, soil slope, normalized-difference vegetative index (NDVI), and soil chemistry. Data were collected from 39–40 30 × 30 m2 locations in each field, four times throughout the growing season, and yield samples were collected manually at the end of the growing season. Four datasets, namely PE-2017, PE-2018, NB-2017, and NB-2018, were then formed by combing data points from three fields to represent the province data for the respective years. Modeling techniques were employed to generate yield predictions assessed with different statistical parameters. The SVR models outperformed all other models for NB-2017, NB-2018, PE-2017, and PE-2018 dataset with RMSE of 5.97, 4.62, 6.60, and 6.17 t/ha, respectively. The performance of k-NN remained poor in three out of four datasets, namely NB-2017, NB-2018, and PE-2017 with RMSE of 6.93, 5.23, and 6.91 t/ha, respectively. The study also showed that large datasets are required to generate useful results using either model. This information is needed for creating site-specific management zones for potatoes, which form a significant component for food security initiatives across the globe.
Farhat Abbas; Hassan Afzaal; Aitazaz A. Farooque; Skylar Tang. Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms. Agronomy 2020, 10, 1046 .
AMA StyleFarhat Abbas, Hassan Afzaal, Aitazaz A. Farooque, Skylar Tang. Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms. Agronomy. 2020; 10 (7):1046.
Chicago/Turabian StyleFarhat Abbas; Hassan Afzaal; Aitazaz A. Farooque; Skylar Tang. 2020. "Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms." Agronomy 10, no. 7: 1046.
Agricultural management practices are responsible for almost two-thirds of the variations in potato tuber yield. In order to answer the research question about the remaining variability of the tuber yield, we hypothesized that climate extremes partly explain the missing component of variations of the tuber yield. Therefore, this research attempts to bridge this knowledge gap in order to generate a knowledge base for future strategies. A climate extreme dataset of the Prince Edward Island (PEI) was computed by averaging the data of five meteorological stations. In detail, changing patterns of 20 climate extreme indices were computed with ClimPACT2 software for 30 years (1989-2018) data of PEI. Statistical significance of the trends and their slope values were determined with the Mann-Kendall test and Sen’s slope estimates, respectively. Average of daily mean temperature (TMm), mean daily minimum temperature (TNm) and the occurrence of continuous dry days (CDD), significantly increased by 0.77 °C, 1.17 °C and 3.33 days., respectively, during the potato growing seasons (May-October) of the past three decades. For this period daily temperature range (DTR), frost days (FD), cold days (TX10p), cold nights (TN10p) and warmest days (TXx) showed decreasing trends of −1.01 °C, −3.75 days, −5.67 days, −11.40 nights, and −2.00 days, respectively. The principal component analysis showed that DTR, TXx, CDD, and TNm were the main factors affecting seasonal variations of tuber yield. The multiple regression model attributed ~39% of tuber yield variance to DTR, TXx, CDD, and TNm. However, these indices explained individually 21%, 19%, 16%, and 4% variation to the tuber yield, respectively. The remaining variation in the tuber yield explained by other yield affecting factors. The information generated from this study can be used for future planning about agricultural management strategies in the Island, for example, the provision of water resources for supplemental irrigation of crops during dry months.
Junaid Maqsood; Aitazaz A. Farooque; Xander Wang; Farhat Abbas; Bishnu Acharya; Hassan Afzaal. Contribution of Climate Extremes to Variation in Potato Tuber Yield in Prince Edward Island. Sustainability 2020, 12, 1 .
AMA StyleJunaid Maqsood, Aitazaz A. Farooque, Xander Wang, Farhat Abbas, Bishnu Acharya, Hassan Afzaal. Contribution of Climate Extremes to Variation in Potato Tuber Yield in Prince Edward Island. Sustainability. 2020; 12 (12):1.
Chicago/Turabian StyleJunaid Maqsood; Aitazaz A. Farooque; Xander Wang; Farhat Abbas; Bishnu Acharya; Hassan Afzaal. 2020. "Contribution of Climate Extremes to Variation in Potato Tuber Yield in Prince Edward Island." Sustainability 12, no. 12: 1.
The question if biochar is a suitable soil nutrient amendment for potato cultivation in the Atlantic Canada is yet to be answered. The objective of this study was to answer this question. Three replicates of twelve lysimeters, each 8000 cm2, were packed with an Atlantic Canada representative soil to cultivate potatoes with four treatments of soil amendments (T1 = control [no added nutrients], T2 = B [biochar], T3 = F [synthetic fertilizer @ recommended NPK], and T4 = B + F [biochar + recommended NPK]) under a completely randomized block design with factorial arrangements. Chemical analyses of soils were conducted for physical, hydrological, and chemical (including concentration of macro- and micro-nutrients) prior to and after the completion experiments to evaluate soil fertility and its resulting effects on crop yield. The biochar amendment improved soil micro- and macro-nutrients. Soil organic matter, pH, and cation exchange capacity (ECE) significantly increased by application of biochar. The maximum potato yield of 30,467.4 kg h−1 was achieved by the combined application of biochar and synthetic fertilizer as this combination resulted in the maximum net benefit ($4433.98 ha−1) in comparison with control treatment that had net loss of $– 2621.49 ha−1. It is therefore concluded that biochar amendment of soils resembling to that of the Atlantic Canada representative soil used in this study, with a mix of recommended NPK for, can formulate a smart precision farming nutrient management technique for this region subject to the field trials and replicate experimental treatments for more than three times.
Aitazaz A. Farooque; Qamar Zaman; Farhat Abbas; Hafiz Mohkum Hammad; Bishnu Acharya; Travis Easu. How can potatoes be smartly cultivated with biochar as a soil nutrient amendment technique in Atlantic Canada? Arabian Journal of Geosciences 2020, 13, 1 -9.
AMA StyleAitazaz A. Farooque, Qamar Zaman, Farhat Abbas, Hafiz Mohkum Hammad, Bishnu Acharya, Travis Easu. How can potatoes be smartly cultivated with biochar as a soil nutrient amendment technique in Atlantic Canada? Arabian Journal of Geosciences. 2020; 13 (9):1-9.
Chicago/Turabian StyleAitazaz A. Farooque; Qamar Zaman; Farhat Abbas; Hafiz Mohkum Hammad; Bishnu Acharya; Travis Easu. 2020. "How can potatoes be smartly cultivated with biochar as a soil nutrient amendment technique in Atlantic Canada?" Arabian Journal of Geosciences 13, no. 9: 1-9.
Climate change induced uneven patterns of rainfall emphasize the use of supplemental irrigation in rainfed agriculture. The Penman–Monteith method was used to calculate supplemental irrigation for water budgeting of a potato crop in Prince Edward Island, Canada. Cumulative gaps between rainfall and crop evapotranspiration (ETc) during August and September of the study years were due to high crop coefficient factor, justifying the need for supplemental irrigation. Pressurized irrigation systems, including sprinklers, fertigation, and drip irrigation were installed, to evaluate the impact of scheduled supplemental irrigation in offsetting deficits in irrigation water requirements in comparison with conventional practice of rainfed cultivation (control). A two-way ANOVA examined the effect of irrigation methods and year on potato tuber yield, water productivity, tuber quality, and payout. Sprinkler and fertigation systems performed better than drip and control treatments. In terms of payout returns and potato tuber quality (percentage of marketable potatoes), the sprinkler treatment performed significantly better than the other treatments. However, for water productivity, fertigation treatment performed significantly better than control and sprinkler treatments during both years. The use of supplemental irrigation is recommended for profitable cultivation of potatoes in soil, agricultural, and environmental conditions resembling to those of Prince Edward Island.
Hassan Afzaal; Aitazaz A. Farooque; Farhat Abbas; Bishnu Acharya; Travis Esau. Precision Irrigation Strategies for Sustainable Water Budgeting of Potato Crop in Prince Edward Island. Sustainability 2020, 12, 2419 .
AMA StyleHassan Afzaal, Aitazaz A. Farooque, Farhat Abbas, Bishnu Acharya, Travis Esau. Precision Irrigation Strategies for Sustainable Water Budgeting of Potato Crop in Prince Edward Island. Sustainability. 2020; 12 (6):2419.
Chicago/Turabian StyleHassan Afzaal; Aitazaz A. Farooque; Farhat Abbas; Bishnu Acharya; Travis Esau. 2020. "Precision Irrigation Strategies for Sustainable Water Budgeting of Potato Crop in Prince Edward Island." Sustainability 12, no. 6: 2419.
Information about potential scenarios and causes of floods is important for future planning. Historical weather data of Fredericton (New Brunswick) and Charlottetown (Prince Edward Island), the two coastal cities of Atlantic Canada, were analyzed using RClimDex, Mann–Kendall test, and Sen’s slope estimates for potential scenarios and causes of floods. Flood hazard analyses were conducted using GIS (Geographical Information System) and ArcSWAT software. The watersheds of Fredericton and Charlottetown were delineated from 25 × 25 m resolution DEMs (Digital Elevation Models) of the two cities followed by percent watershed area calculations for different elevation classes for flood generation. Over the past 100 years, there was a significant decreasing trend in the high intensity precipitation in Charlottetown supported by a significant decrease in the number of heavy precipitation days. However, maximum one-day precipitation and maximum five-day precipitation significantly increased in Charlottetown and Fredericton, respectively. Charlottetown received more annual precipitation than Fredericton. In the last 30 years, there was an event exceeding 50 mm precipitation (considered as a threshold for the return period of urban floods) in Charlottetown; Fredericton experienced such events for more than 1.5 times. For twelve times, these events occurred more than once in a year in Charlottetown as compared to fourteen times in Fredericton. Despite statistically proven similarities in the occurrence of extreme events in the two cities, the visualized flood hazards, and the mapping of watershed characteristics, no devastating floods were reported for Charlottetown. This does not necessarily mean that there had never been risks of flooding in Charlottetown. These findings may help policymakers for future developments.
Farhat Abbas; Aitazaz A. Farooque; Hassan Afzaal. Homogeneity in Patterns of Climate Extremes Between Two Cities—A Potential for Flood Planning in Relation to Climate Change. Water 2020, 12, 782 .
AMA StyleFarhat Abbas, Aitazaz A. Farooque, Hassan Afzaal. Homogeneity in Patterns of Climate Extremes Between Two Cities—A Potential for Flood Planning in Relation to Climate Change. Water. 2020; 12 (3):782.
Chicago/Turabian StyleFarhat Abbas; Aitazaz A. Farooque; Hassan Afzaal. 2020. "Homogeneity in Patterns of Climate Extremes Between Two Cities—A Potential for Flood Planning in Relation to Climate Change." Water 12, no. 3: 782.
Accurate estimation of reference evapotranspiration (ETo) provides useful information for water resource management and sustainable agriculture. This study estimates ETo with recurrent neural networks (RNNs), namely long short-term memory (LSTM) and bidirectional LSTM. Four representative meteorological sites (North Cape, Summerside, Harrington, and Saint Peters) were selected across Prince Edward Island (PEI), Canada to form a PEI dataset from mean values of the four sites’ climatic variables for capturing climatic variability from all parts of the province. Based on subset regression analysis, the highest contributing climatic variables, namely maximum air temperature and relative humidity, were selected as input variables for RNNs’ training (2011–2015) and testing (2016–2017) runs. The results suggested that the LSTM and bidirectional LSTM are suitable methods to accurately (R2 > 0.90) estimate ETo for all sites except Harrington. Testing period (2016–2017) root mean square errors were recorded in range of 0.38–0.58 mm/day for all sites. No major differences were observed in accuracy of LSTM and bidirectional LSTM. Another objective of this study was to highlight the potential gap between ETO and rainfall for assessing agriculture sustainability in Prince Edward Island. Analyses of the data highlighted that the cumulative ETo surpassed the cumulative rainfall potentially affecting yield of major crops in the island. Therefore, agriculture sustainability requires viable options such as supplemental irrigation to replenish the crop water requirements as and when needed.
Hassan Afzaal; Aitazaz A. Farooque; Farhat Abbas; Bishnu Acharya; Travis Esau. Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management. Applied Sciences 2020, 10, 1621 .
AMA StyleHassan Afzaal, Aitazaz A. Farooque, Farhat Abbas, Bishnu Acharya, Travis Esau. Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management. Applied Sciences. 2020; 10 (5):1621.
Chicago/Turabian StyleHassan Afzaal; Aitazaz A. Farooque; Farhat Abbas; Bishnu Acharya; Travis Esau. 2020. "Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management." Applied Sciences 10, no. 5: 1621.
Precise estimation of physical hydrology components including groundwater levels (GWLs) is a challenging task, especially in relatively non-contiguous watersheds. This study estimates GWLs with deep learning and artificial neural networks (ANNs), namely a multilayer perceptron (MLP), long short term memory (LSTM), and a convolutional neural network (CNN) with four different input variable combinations for two watersheds (Baltic River and Long Creek) in Prince Edward Island, Canada. Variables including stream level, stream flow, precipitation, relative humidity, mean temperature, evapotranspiration, heat degree days, dew point temperature, and evapotranspiration for the 2011–2017 period were used as input variables. Using a hit and trial approach and various hyperparameters, all ANNs were trained from scratched (2011–2015) and validated (2016–2017). The stream level was the major contributor to GWL fluctuation for the Baltic River and Long Creek watersheds (R2 = 50.8 and 49.1%, respectively). The MLP performed better in validation for Baltic River and Long Creek watersheds (RMSE = 0.471 and 1.15, respectively). Increased number of variables from 1 to 4 improved the RMSE for the Baltic River watershed by 11% and for the Long Creek watershed by 1.6%. The deep learning techniques introduced in this study to estimate GWL fluctuations are convenient and accurate as compared to collection of periodic dips based on the groundwater monitoring wells for groundwater inventory control and management.
Hassan Afzaal; Aitazaz A. Farooque; Farhat Abbas; Bishnu Acharya; Travis Esau. Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and Deep Learning. Water 2019, 12, 5 .
AMA StyleHassan Afzaal, Aitazaz A. Farooque, Farhat Abbas, Bishnu Acharya, Travis Esau. Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and Deep Learning. Water. 2019; 12 (1):5.
Chicago/Turabian StyleHassan Afzaal; Aitazaz A. Farooque; Farhat Abbas; Bishnu Acharya; Travis Esau. 2019. "Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and Deep Learning." Water 12, no. 1: 5.
Timely forecasting of crop yield is vital for precision agriculture management practices. This study used on‐the‐go proximal soil sensing using electromagnetic induction (EMI) readings of apparent ground electrical conductivity (ECa) to map ECa and forecast potato tuber yield in four fields across Atlantic Canada. The ECa data, measured in the horizontal co‐planar (HCP) configuration mode of the DualEM‐2 instrument, were segmented to the top 0.30 m thickness of the soil layer using a standard method to compare mapping/prediction accuracy. Results showed that ECa correlated well (R2 = 0.81–0.90) with a 1:5 soil‐to‐water ratio solutions’ electrical conductivity (EC1:5). The actual tuber yield, which moderately varied (CV = 18.9–27.5%) across the fields and significantly correlated with ECa, explained more than 55% of the yield variability (R2 = 0.57–0.66). The forecasted tuber yield calculated from cubic regression models of the actual tuber yield versus ECa was non‐significantly different from the actual tuber yield (RMSE = 12.2–18.3%; R2 = 0.57–0.66). Interpolated maps of the predicted and the actual yields, and their correlation analyses, showed similar trends of variations within the study fields (r = 0.69–0.80). The higher values of cation exchange capacity (CEC), calcium (Ca), phosphate (P), potash (K), organic matter and soil moisture content (θ) in the New Brunswick soils compared to the Prince Edward Island soils resulted in an overestimation of the predicted tuber yield than the actual yield at the lower ECa values, and an underestimation of the predicted tuber yield at higher ECa values for New Brunswick. The results revealed that the province‐based calibrations produced more accurate predictions when compared with the single calibration by combining all of the data from New Brunswick and Prince Edward Island. The non‐destructive prediction of potato tuber yield can enable the development of precision agricultural techniques and management practices for yield forecasting, in addition to making informed decisions for enhanced potato productivity. This article is protected by copyright. All rights reserved.
Aitazaz A. Farooque; Mahnaz Zare; Farhat Abbas; Melanie Bos; Travis Esau; Qamar Zaman. Forecasting potato tuber yield using a soil electromagnetic induction method. European Journal of Soil Science 2019, 1 .
AMA StyleAitazaz A. Farooque, Mahnaz Zare, Farhat Abbas, Melanie Bos, Travis Esau, Qamar Zaman. Forecasting potato tuber yield using a soil electromagnetic induction method. European Journal of Soil Science. 2019; ():1.
Chicago/Turabian StyleAitazaz A. Farooque; Mahnaz Zare; Farhat Abbas; Melanie Bos; Travis Esau; Qamar Zaman. 2019. "Forecasting potato tuber yield using a soil electromagnetic induction method." European Journal of Soil Science , no. : 1.
Spatial variability of soil physical and hydrological properties within or among agricultural fields could be intrinsically induced due to geologic and pedologic soil forming factors, but some of the variability may be induced by anthropogenic activities such as tillage practices. No-tillage has been gaining ground as a successful conservation practice, and quantifying spatial variability of soil physical properties induced by no-tillage practices is a prerequisite for making appropriate site-specific agricultural management decisions and/or reformulating some management practices. In particular, there remains very limited information on the spatial variability of soil physical properties under long-term no-tillage corn and tropical soil conditions. Therefore, the main objective of this study was to quantify the spatial variability of some selected soil physical properties (soil surface temperature (ST), volumetric water content (θv), soil resistance (TIP), total porosity (θt), bulk density (ρb), organic carbon, and saturated hydraulic conductivity (Ksat)) using classical and geostatistical methods. The study site was a 2 ha field cropped no-tillage sweet corn for nearly 10 years on Oahu, Hawaii. The field was divided into 10 × 10 and 20 × 20 m grids. Soil samples were collected at each grid for measuring ρb, θt, and soil organic carbon (SOC) in the laboratory following standard methods. Saturated hydraulic conductivity, TIP at 10 and 20 cm depths, soil surface temperature, and θv were also measured. Porosity and ρb have low and low to moderate variability, respectively based on the relative ranking of the magnitude of variability drawn from the coefficient of variation. Variability of the SOC, TIP, and Ksat ranges from moderate to high. Based on the best-fitted semivariogram model for finer grid data, 9.8 m and 142.2 m are the cut off beyond which the measured parameter does not show any spatial correlation for SOC, and TIP at 10 cm depth, respectively. Bulk density shows the highest spatial dependence (range = 226.8 m) among all measured properties. Spatial distribution of the soil properties based on kriging shows a high level of variability even though the sampled field is relatively small.
Ripendra Awal; Mohammad Safeeq; Farhat Abbas; Samira Fares; Sanjit K. Deb; Amjad Ahmad; Ali Fares. Soil Physical Properties Spatial Variability under Long-Term No-Tillage Corn. Agronomy 2019, 9, 750 .
AMA StyleRipendra Awal, Mohammad Safeeq, Farhat Abbas, Samira Fares, Sanjit K. Deb, Amjad Ahmad, Ali Fares. Soil Physical Properties Spatial Variability under Long-Term No-Tillage Corn. Agronomy. 2019; 9 (11):750.
Chicago/Turabian StyleRipendra Awal; Mohammad Safeeq; Farhat Abbas; Samira Fares; Sanjit K. Deb; Amjad Ahmad; Ali Fares. 2019. "Soil Physical Properties Spatial Variability under Long-Term No-Tillage Corn." Agronomy 9, no. 11: 750.