Temporal and operation-induced instability of apparent soil electrical conductivity measurements

Measuring apparent soil electrical conductivity (ECa), using galvanic contact resistivity (GCR) and electromagnetic induction (EMI) techniques, is frequently conducted to reveal spatial soil heterogeneity. Various studies have demonstrated the possibilities for significant changes in the measured quantities over time with relatively stable spatial structure representations. The objective of this study was to quantify the effects of temporal drift and operational noise for three popular ECa mapping instruments. They were placed in stationary positions approximately 8 m apart in an area with relatively low ECa. Temporal drift was assessed using a series of 4.5-h data logs recorded under different weather conditions (from extremely hot to near freezing temperatures). The two EMI instruments were also used to quantify the effect of minor changes in the height, pitch and roll of the sensor with respect to the ground. These operational noise tests were replicated over several days. Our results reveal the GCR measurements of ECa, along with perpendicular coplanar EMI measurements, have shown relatively strong stability over time. Each operational effect introduced measurement uncertainties comparable to the impact of a change in temperature and soil water content

Nitrous oxide and carbon dioxide emissions from surface and subsurface drip irrigated tomato fields

Irrigation practices change the soil moisture in agricultural fields and influence emissions of greenhouse gases (GHG). A 2 yr field study was conducted to assess carbon dioxide (CO2) and nitrous oxide (N2O) emissions from surface and subsurface drip irrigated tomato (Solanum lycopersicum L.) fields on a loamy sand in southern Ontario. Surface and subsurface drip irrigation are common irrigation practices used by tomato growers in southern Ontario. The N2O fluxes were generally ≤50 μg N2O-N m⁻² h⁻¹, with mean cumulative emissions ranging between 352 ± 83 and 486 ± 138 mg N2O-N m⁻². No significant difference in N2O emissions between the two drip irrigation practices was found in either study year. Mean CO2 fluxes ranged from 22 to 160 mg CO2-C m² h⁻¹ with cumulative fluxes between 188 ± 42 and 306 ± 31 g CO2-C m⁻². Seasonal CO2 emissions from surface drip irrigation were significantly greater than subsurface drip irrigation in both years, likely attributed to sampling time temperature differences. We conclude that these irrigation methods did not have a direct effect on the GHG emissions from tomato fields in this study. Therefore, both irrigation methods are expected to have similar environmental impacts and are recommended to growers

Drone payload and flying speed effects on rotor blades' RPM and traveling pattern for agricultural chemical spraying

The sprayed chemicals by drones have been widely reported to be off-targeted and not uniformly distributed. This study aims to evaluate the drone blade’s revolutions per minute (RPM) and its travelling pattern at different payloads and flight speeds. The obtained results were used to relate to the potential effects on the quantity and quality of spraying. In a test flight on an area of 1000 m2, a hexacopter, Advansia A1 was tested in 6 different flying paths of 56 m length. The drone was set to fly at 5 payloads (10, 8, 6, 4, and 2 kg) and 4 flying speeds (i.e. 1, 3, 5, and 7 m.s-1) combinations. The drone travelling pattern and individual rotor blade rpm at each payload-flying speed combinations were analysed. From the result, the RPM of each rotor blade were found to decrease by 14 to 20% as the payload was decreased from 10kg to 0kg. Thus, in actual spraying activities, the changes in RPM could produce a downwash airflow pattern that continually varies from starting point up to the finishing point that would effect on pesticide's distribution along the flying path. On drone travelling pattern, at higher flying speed, a much lesser time and distance was required for the drone to be stabilized to the targeted speed. This relates to the longer time needed by the drone to accelerate and decelerate. The average real speed of the drone was notably reduced to 0.96, 2.72, 3.83 and 4.05 m.s-1, in which, it was, far less than the initial specified speed set at 1, 3, 5, and 7 m.s-1, respectively. The drone flying pattern during spraying needs to be considered for application rate determination to avoid for the crops to be under or over pesticide applications. The obtained finding is remarkably critical and useful in ensuring the efficiency of agricultural chemical spraying activities using drone

Geotagged application for durian trees using aerial imagery and vegetation indices algorithm

Durian demand has increased considerably, and it has gained popularity in the market. Under Industrial Revolution 4.0, precision agriculture is expanding globally with a wide range of digital technologies that provide the farming industry with information to improve farm productivity. The objectives of this study are to geotag the durian trees and to compare several Vegetation Indices (VIs) algorithms (VisibleBand Difference Vegetation Index (VDVI), Visible Atmospherically Resistant Index (VARI), Normalized Green-Red Difference Index (NGRDI), Red-Green Ratio Index (RGRI), Modified Green-Red Vegetation Index (MGRVI), Excess Green Index (ExG), Color Index of Vegetation (CIVE), and Vegetativen (VEG)). One hundred sixty durian trees at the Durian Valley in Kluang (Johor), were tagged, which consist of four sample trees for each treatment. Every two weeks of ground data such as the height of trees, canopy width, girth’s diameter, node distance, pH value, moisture content, electrical conductivity (EC) reading, and leaf sizes were exported into the QGIS software and joined with the tagged durian trees. The aerial imagery data captured the durian plantation area using Red Green Blue (RGB) sensor with a 100 m flight attitude. pH, EC, and moisture content were interpolated using Inverse Distance Weighted (IDW) technique. The processed image by VIs and geotagged trees could help farmers to identify the problem areas in the farm and monitor durian plantation effectively

Denial of long-term issues with agriculture on tropical peatlands will have devastating consequences

Non peer reviewe

Application of proximal soil sensing for environmental characterization of agricultural land

Sustainable high-intensity agriculture involves optimizing yield and profitability without compromising the environment. High chemical inputs have the potential to accelerate soil systems' biological activity and emission of greenhouse gasses (GHG; e.g., CO2, CH4, and N2O) without increasing the yield. Quantifying emissions from agricultural soils is critical to assessing the sustainability of farming practices. Usually, estimates of agriculture-driven GHG emissions are based on a small number of sampling sites. Inherent differences in soil climatic and physical properties and crop management activities can significantly affect an agricultural field's spatial and temporal patterns of GHG emissions. Accordingly, a close knowledge of soil heterogeneity is critical for improving the reliability of GHG emission estimates. In this project, stability estimates of apparent soil electrical conductivity (ECa) measurements by electromagnetic induction (EMI) and galvanic contact resistance (GCR) instruments were assessed by testing for both temporal and operational effects of a sodden lawn (soil ECa = 5 15 ms m 1). Operational effects on the instrument included height above ground (0 or 0.10 m), roll angle (0ᵒ and ±10ᵒ), and pitch angle (0ᵒ and ±10ᵒ). Among EMI measurements, the perpendicular coplanar (PRP) operating mode of the DUALEM–21S provided the most stable measurements. Changes in height and roll within tolerance had no effect on soil ECa measurements, but increasing pitch reduced measurement values. From a practical point of view, soil ECa measurements varied little within the height tolerance of 0.10 m, and roll and pitch tolerance of ±10ᵒ. In a second study, a database management methodology was developed to analyze the >30,000 GHG samples. This methodology included a means for data format standardization and flux/emission calculation based on 103 fixed sampling locations across Eastern Canada using a suite of automated MATLAB scripts. Flux estimates were determined using the median slope of temporal change of concentration, thereby filtering outliers arising from erroneous measurements. In a third study, temporal variations in GHG emissions under different soil physical properties and soil organic matter decomposition rates were monitored in three sites with replicated water treatment plots (sprinkler irrigation vs. no irrigation), using a network of wireless sensors that monitored soil matric potential, volumetric water content and soil temperature. Muck soils tended to emit more N2O under relatively wet and cool conditions, whereas CH4 fluxes peaked in fully wet soils, while moderate soil moisture levels and warm temperatures promoted CO2 emissions. Correlations between GHG fluxes and measured soil properties were rather weak, limiting the potential for modeling GHG fluxes and emissions. In a fourth and final study, placement of GHG monitoring sites was optimized for an agricultural field with variable soil conditions. Nine locations were selected and monitored to detect levels of GHG fluxes and emissions representing the most extreme soil conditions present in the chosen field. Different soil types, as well as soil moisture and temperature dynamics, resulted in different levels of GHG emissions. Due to high soil moisture content caused by a field depression, methane emissions were highest in muck (vs. mineral) soils. Assessment of spatial and temporal variations in soil physical characteristics can clarify GHG emission dynamics, allowing a more accurate quantification of modern farming systems' environmental impact.L'agriculture durable peut être définie comme l'optimisation du rendement et la rentabilité sans compromettre l'environnement. Nécessitant des taux élevés de composés chimiques, qui accélèrent l'activité biologique des sols, les pratiques d'agriculture intensive entraînent l'émission de gaz à effet de serre (GES; CO2, CH4, et N2O), sans pour autant obtenir de meilleurs rendements. Quantifier les émissions de GES provenant des sols agricoles devient alors essentiel lors de l'évaluation de la durabilité des pratiques agricoles. N'ayant pas tenu compte de l'hétérogénéité des champs, les estimations d'émissions de gaz du passé ne consistaient qu'en une extrapolation à partir des émissions d'un petit nombre de sites d'échantillonnage. Des différences inhérentes quant aux propriétés physiques et climatiques des sols et des activités de gestion des cultures peuvent affecter significativement la répartition spatiale (à l'horizontale et en profondeur) et temporelle des émissions de GES. Conséquemment, l'amélioration de la fiabilité d'estimation des émissions de GES est étroitement liée à une connaissance approfondie de l'hétérogénéité des sols. En une première étude, la qualité de la cartographie de la conductivité apparente du sol (ECa), évaluée par induction électromagnétique (IEM) avec des instruments de contact à résistance galvanique (CRG) fut évalués en examinant les effets temporels et opérationnels d'une pelouse trempée (ECa = 5-15 ms m-1). Les effets opérationnels ont inclus la distance au-dessus de la surface (0 ou 0.10 m), et les angles de roulis et de tangage (0ᵒ et ±10ᵒ). Parmi les mesures d'EMI, le capteur DUALEM–21S en mode d'opération de conductivité perpendiculaire coplanaire (CPC) a offert les mesures les plus stables. Les variations en hauteur et roulis inférieures à la tolérance évaluée n'affectèrent pas l'ECa du sol, mais les variations en tangage par rapport à 0° ont réduit l'ECa. D'un point de vue pratique, les mesures de l'ECa du sol ont très peu varié à l'intérieur d'une tolérance en hauteur de 0.10 m, et de roulis et tangage de ±10ᵒ. Face à de gros volumes de données (>30,000 échantillons de gaz) provenant de multiples (103 chambres à gaz à 6 sites) et divers (c.-à-d. sol, récolte, irrigation) emplacements dans l'est du Canada, une seconde étude s'adressa à la gestion des bases de données, le format de normalisation, et aux problèmes de calcul des flux et émissions. Pour ce faire, une série de scriptes MATLAB furent développés. L'estimation des flux et émissions utilisa les pentes médianes de régressions linéaires, une méthode permettant de filtrer les mesures erronées. Dans une troisième étude, les variations temporelles des émissions de GES sous différentes combinaisons de propriétés physiques et taux de décomposition de sols organiques à trois sites disposant de parcelles irriguées ou non irriguées furent suivies à l'aide de réseaux de capteurs sans fil pour le potentiel matriciel du sol, l'humidité du sol, et sa température. Les sols de terre noire émirent plus de N2O lorsque le sol était relativement humide et les conditions climatiques fraîches, tandis que les flux en CH4 furent plus élevés dans les sols détrempés. Les émissions de CO2 furent le plus élevées lorsque l'humidité du sol était modérée et les températures chaudes. Les corrélations entre les flux de GES et les propriétés du sol se révélèrent plutôt faibles, limitant le potentiel de modélisation des flux et des émissions de GES. Les émissions en CH4 étaient particulièrement élevées pour les terres noires (vs sols minéraux) principalement en raison de dépressions dans le champ et de la haute teneur en eau du sol dans ces dépressions. L'évaluation des variations spatiales et temporelles dans les caractéristiques physiques des sols peut aider à mieux comprendre la dynamique des émissions de GES, permettant ainsi de quantifier plus précisément les effets environnementaux des systèmes agricoles modernes

Gamification approach toward flexibility learning: MyHSC apps

In the context of the increasingly changing and evolving landscape of higher education, flexible learning is the future of higher education which brings the goal of access to education at any time, by anyone, and anywhere. The approach to flexible learning can be improved through innovations that are recognized as gamification. The smartphones application such as Android or iOS platform has enabled stakeholders in biodiversity to connect the ubiquity of these platforms and create various types of mobile applications as a tool in biodiversity and conservation research. The interactive functionalities such as gamification via smartphone have designed for public and biodiversity researchers to identify, obtain and record biodiversity data as well as generating knowledge portability in the digital forms. The gamification involves the use of game elements in non-game contexts and enable users to utilize computational ability to discover the enjoyment of species identification which previously reserved for biodiversity experts. However, apparently there is lack of mobile application gamification devoted to the identification in horseshoe crab species. This gamification of species identification app facilitated user to identify the species as well as accompanying with field guide. This innovation enable user (student/public) to comprise with main species information, image capturing, geolocation and geotagging which will assists and ease the users to identify and record the information during conservation/field work. Perhaps, MyHSC app gamification of horseshoe crab species identification will create an impetus to the interest and enhancement engagement of student in species identification as well as increasing awareness among public for species conservation in Malaysia

Towards Agriculture 4.0: impact of precision agriculture technology among Malaysia's producers

There is no doubt that technological advancement eases the routine works of the farmers, alleviate the load, to solve the daily problem faced, as well as for fast decision making. Agriculture 4.0 (Ag4.0) is about boosting the agriculture production by changing the conventional mechanism of producing food, with more advance methods and technique. This includes the used of smart machineries, intelligent robotics equipped with sensors and devices, information technologies and sophisticated decision making tools. The readiness of the technological advancement is highly associated with the economic evaluation and justification, but not limited to a certain type of agricultural production. Precision Agriculture (PA) technologies serve as a tool to manage the farm as well as the crop production activities. The PA serves as a transitional progress between the conventional methods towards Ag4.0 under a new agricultural industry revolution. This will pave a way towards the Agriculture 5.0 on the artificial intelligence (AI) and smart farming production. The megatrends in agriculture sector on Malaysia perspective are, produce differently using a new technique, utilizing the new technology to bring the food to consumers, increasing the efficiency in the food chain and incorporates cross-industry technique and applications

A review of fertilization assessment methods in oil palm plantation

Rising in production cost due to labour shortage, low efficiency of farm operation, low yield, and increase in input cost of materials were among the key factors faced by Malaysian oil palm industry. Thus, significantly affect the overall upstream performance and the annual budget for the plantation operation. Input cost, especially fertilizer accounted for more than 50% of the production cost annually. Highly weathered tropical soil, intensive and mono-cropping farming activities caused nutrient depletion over the availability of the nutrient to the plant. Among the agronomic practices, fertilizer application and saving become a limitation due to its increasing share of production cost, thus effect the annual targeted yield. Prior to the fertilizer application and recommendation rate, a proper fertilizer assessment program should be conducted for achieving economic, social and environmental sustainability. In this paper, several methods of fertilizer assessment for oil palm plantation were identified and discussed based upon the agronomic practices. In addition, the several techniques under proximal sensing technology for Precision Agriculture (PA) program to quantify the fertility and crop response also were listed. The adaptation of the technology and a new approach in reducing the fertilizer used will lead the input cost reduction in oil palm plantation while improves overall farm efficiency