Evaluating system of rice intensification using a modified transplanter: A smart farming solution toward sustainability of paddy fields in Malaysia

This paper presents the study reports on evaluating a new transplanting operation by taking into accounts the interactions between soil, plant, and machine in line with the System of Rice Intensification (SRI) practices. The objective was to modify planting claw (kuku-kambing) of a paddy transplanter in compliance with SRI guidelines to determine the best planting spacing (S), seed rate (G) and planting pattern that results in a maximum number of seedling, tillers per hill, and yield. Two separate experiments were carried out in two different paddy fields, one to determine the best planting spacing (S=4 levels: s1=0.16 m×0.3 m, s2= 0.18 m×0.3 m, s3=0.21 m×0.3 m, and s4=0.24 m×0.3 m) for a specific planting pattern (row mat or scattered planting pattern), and the other to determine the best combination of spacing with seed rate treatments (G=2 levels: g1=75 g/tray, and g2= 240 g/tray). Main SRI management practices such as soil characteristics of the sites, planting depth, missing hill, hill population, the number of seedling per hill, and yield components were evaluated. Results of two-way analysis of variance with three replications showed that spacing, planting pattern and seed rate affected the number of one-seedling in all experiment. It was also observed that the increase in spacing resulted in more tillers and more panicle per plant, however hill population and sterility ratio increased with the decrease in spacing. While the maximum number of panicles were resulted from scattered planting at s4=0.24 m×0.3 m spacing with the seed rate of g1=75 g/tray, the maximum number of one seedling were observed at s4=0.16 m×0.3 m. The highest and lowest yields were obtained from 75 g seeds per tray scattered and 70 g seeds per tray scattered treatment respectively. For all treatments, the result clearly indicates an increase in yield with an increase in spacing.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

An agricultural investment map based on geographic information system and multi-criteria method.

The study aimed to produce an investment classification map, which shows the potential areas of investment in agriculture in Sinnar, Sudan. The spatial multi-criteria analysis was used to rank and display potential locations, while the analytical hierarchy process method was used to compute the priority weights of each criterion. The study attempted to explore the utilization of Geographic Information System (GIS) to map the potential investment areas, therefore, it did not cover a comprehensive analysis of all factors that influence investment in agriculture. In addition, the analysis was limited to criteria that had spatial reference. The investment criteria for spatial analysis were defined from the guidelines provided by the Ministry of Investment, Sudan. Even with the shortcomings of the data, it was found that the results obtained were very encouraging and provided clear indicative areas for agricultural investment in Sinnar. Government agencies can use GIS to access information regarding the potential areas of investment, and minimize investment risks. On the other hand, the economic development organizations will now have the ability to benefit from the Geographic Information System (GIS) solutions by leveraging on this technology to attract and retain business from worldwide sources. Thus, the model will serve as a decision support tool for investors and decision makers at various levels

Scientific Irrigation Scheduling for Sustainable Production in Olive Groves

The present study aimed at investigating scientific irrigation scheduling (SIS) for the sustainable production of olive groves. The SIS allows farmers to schedule water rotation in their fields to abate crop water stress and maximize yields, which could be achieved through the precise monitoring of soil moisture. For this purpose, the study used three kinds of soil moisture sensors, including tensiometer sensors, irrometer sensors, and gypsum blocks for precise measurement of the soil moisture. These soil moisture sensors were calibrated by performing experiments in the field and laboratory at Barani Agricultural Research Institute, Chakwal in 2018 and 2019. The calibration curves were obtained by performing gravimetric analysis at 0.3 and 0.6 m depths, thereby equations were developed using regression analysis. The coefficient of determination (R2 ) at 0.3 and 0.6 m depth for tensiometer, irrometer, and gypsum blocks was found to be equal to 0.98, 0.98; 0.75, 0.89; and 0.82, and 0.95, respectively. After that, a drip irrigation system was installed with the calibrated soil moisture sensors at 0.3 and 0.6 m depth to schedule irrigation for production of olive groves as compared to conventional farmer practice, thereby soil moisture profiles of these sensors were obtained to investigate the SIS. The results showed that the irrometer sensor performed as expected and contributed to the irrigation water savings between 17% and 25% in 2018 and 2019, respectively, by reducing the number of irrigations as compared toother soil moisture sensors and farmer practices. Additionally, olive yield efficiencies of 8% and 9%were observed by the tensiometer in 2018 and 2019, respectively. The outcome of the study suggests that an effective method in providing sustainable production of olive groves and enhancing yield efficiency

Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran

Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environmental parameters, including temperature, relative humidity, wind speed, air pressure, and vegetation, interact with air pollutants, such as particulate matter (PM), NO2, SO2, O3, and CO, contributing to frameworks for forecasting air quality. The objective of the present study is to explore these interactions in three Iranian metropolises of Tehran, Tabriz, and Shiraz from 2015 to 2019 and develop a machine learning-based model to predict daily air pollution. Three distinct assessment criteria were used to assess the proposed XGBoost model, including R squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Preliminary results showed that although air pollutants were significantly associated with meteorological factors and vegetation, the formulated model had low accuracy in predicting (R2PM2.5 = 0.36, R2PM10 = 0.27, R2NO2 = 0.46, R2SO2 = 0.41, R2O3 = 0.52, and R2CO = 0.38). Accordingly, future studies should consider more variables, including emission data from manufactories and traffic, as well as sunlight and wind direction. It is also suggested that strategies be applied to minimize the lack of observational data by considering second-and third-order interactions between parameters, increasing the number of simultaneous air pollution and meteorological monitoring stations, as well as hybrid machine learning models based on proximal and satellite data

IoT-Based Sensor Data Fusion for Determining Optimality Degrees of Microclimate Parameters in Commercial Greenhouse Production of Tomato

Optimum microclimate parameters, including air temperature (T), relative humidity (RH) and vapor pressure deficit (VPD) that are uniformly distributed inside greenhouse crop production systems are essential to prevent yield loss and fruit quality. The objective of this research was to determine the spatial and temporal variations in the microclimate data of a commercial greenhouse with tomato plants located in the mid-west of Iran. For this purpose, wireless sensor data fusion was incorporated with a membership function model called Optimality Degree (OptDeg) for real-time monitoring and dynamic assessment of T, RH and VPD in different light conditions and growth stages of tomato. This approach allows growers to have a simultaneous projection of raw data into a normalized index between 0 and 1. Custom-built hardware and software based on the concept of the Internet-of-Things, including Low-Power Wide-Area Network (LoRaWAN) transmitter nodes, a multi-channel LoRaWAN gateway and a web-based data monitoring dashboard were used for data collection, data processing and monitoring. The experimental approach consisted of the collection of meteorological data from the external environment by means of a weather station and via a grid of 20 wireless sensor nodes distributed in two horizontal planes at two different heights inside the greenhouse. Offline data processing for sensors calibration and model validation was carried in multiple MATLAB Simulink blocks. Preliminary results revealed a significant deviation of the microclimate parameters from optimal growth conditions for tomato cultivation due to the inaccurate timer-based heating and cooling control systems used in the greenhouse. The mean OptDeg of T, RH and VPD were 0.67, 0.94, 0.94 in January, 0.45, 0.36, 0.42 in June and 0.44, 0.0, 0.12 in July, respectively. An in-depth analysis of data revealed that averaged OptDeg values, as well as their spatial variations in the horizontal profile were closer to the plants’ comfort zone in the cold season as compared with those in the warm season. This was attributed to the use of heating systems in the cold season and the lack of automated cooling devices in the warm season. This study confirmed the applicability of using IoT sensors for real-time model-based assessment of greenhouse microclimate on a commercial scale. The presented IoT sensor node and the Simulink model provide growers with a better insight into interpreting crop growth environment. The outcome of this research contributes to the improvement of closed-field cultivation of tomato by providing an integrated decision-making framework that explores microclimate variation at different growth stages in the production season

Effects of the COVID-19 pandemic on food security and agriculture in Iran: a survey

The consequences of COVID-19 on the economy and agriculture have raised many concerns about global food security, especially in developing countries. Given that food security is a critical component that is affected by global crises, beside the limited studies carried out on the macroimpacts of COVID-19 on food security in Iran, this paper is an attempt to address the dynamic impacts of COVID-19 on food security along with economic and environmental challenges in Iran. For this purpose, a survey was conducted with the hypothesis that COVID-19 has not affected food security in Iran. To address this fundamental hypothesis, we applied the systematic review method to obtain the evidence. Various evidences, including indices and statistics, were collected from national databases, scientific reports, field observations, and interviews. Preliminary results revealed that COVID-19 exerts its effects on the economy, agriculture, and food security of Iran through six major mechanisms, corresponding to a 30% decrease in the purchasing power parity in 2020 beside a significant increase in food prices compared to 2019. On the other hand, the expanding environmental constraints in Iran reduce the capacity of the agricultural sector to play a crucial role in the economy and ensure food security, and in this regard, COVID-19 forces the national programs and budget to combat rising ecological limitations. Accordingly, our study rejects the hypothesis that COVID-19 has not affected food security in Iran

Digital Agriculture in Iran: Use Cases, Opportunities, and Challenges

Agriculture is constantly developing into a progressive sector by benefiting from a variety of high-tech solutions with the ultimate objectives of improving yield and quality, minimizing wastes and inputs, and maximizing the sustainability of the process. For the case of Iran, adaptation of digital agriculture is one of the key economic plans of the government until 2025. For this purpose, the development of infrastructure besides understanding social and cultural impacts on the transformation of traditional agriculture is necessary. This chapter reports the potential of the existing technological advances and the state of the current research efforts for the implementation of digital agriculture in open-field and closed-field crop production systems in Iran. The focus of the study was on the development of affordable IoT devices and their limitations for various farming applications including smart irrigations and crop monitoring, as well as an outlook for the use of robotics and drone technology by local farmers in Iran

Spatial Distribution Patterns for Identifying Risk Areas Associated with False Smut Disease of Rice in Southern India

False smut disease (FSD) of rice incited by Ustilaginoidea virens is an emerging threat to paddy cultivation worldwide. We investigated the spatial distribution of FSD in different paddy ecosystems of South Indian states, viz., Andhra Pradesh, Karnataka, Tamil Nadu, and Telangana, by considering the exploratory data from 111 sampling sites. Point pattern and surface interpolation analyses were carried out to identify the spatial patterns of FSD across the studied areas. The spatial clusters of FSD were confirmed by employing spatial autocorrelation and Ripley’s K function. Further, ordinary kriging (OK), indicator kriging (IK), and inverse distance weighting (IDW) were used to create spatial maps by predicting the values at unvisited locations. The agglomerative hierarchical cluster analysis using the average linkage method identified four main clusters of FSD. From the Local Moran’s I statistic, most of the areas of Andhra Pradesh and Tamil Nadu were clustered together (at I > 0), except the coastal and interior districts of Karnataka (at I < 0). Spatial patterns of FSD severity were determined by semi-variogram experimental models, and the spherical model was the best fit. Results from the interpolation technique, the potential FSD hot spots/risk areas were majorly identified in Tamil Nadu and a few traditional rice-growing ecosystems of Northern Karnataka. This is the first intensive study that attempted to understand the spatial patterns of FSD using geostatistical approaches in India. The findings from this study would help in setting up ecosystem-specific management strategies to reduce the spread of FSD in India

Health promoting potential of herbal teas and tinctures from Artemisia campestris subsp maritima: from traditional remedies to prospective products

This work explored the biotechnological potential of the medicinal halophyte Artemisia campestris subsp. maritima (dune wormwood) as a source of health promoting commodities. For that purpose, infusions, decoctions and tinctures were prepared from roots and aerial-organs and evaluated for in vitro antioxidant, anti-diabetic and tyrosinase-inhibitory potential, and also for polyphenolic and mineral contents and toxicity. The dune wormwood extracts had high polyphenolic content and several phenolics were identified by ultra-high performance liquid chromatography-photodiode array-mass-spectrometry (UHPLC-PDA-MS). The main compounds were quinic, chlorogenic and caffeic acids, coumarin sulfates and dicaffeoylquinic acids; several of the identified phytoconstituents are here firstly reported in this A. campestris subspecies. Results obtained with this plant's extracts point to nutritional applications as mineral supplementary source, safe for human consumption, as suggested by the moderate to low toxicity of the extracts towards mammalian cell lines. The dune wormwood extracts had in general high antioxidant activity and also the capacity to inhibit a-glucosidase and tyrosinase. In summary, dune wormwood extracts are a significant source of polyphenolic and mineral constituents, antioxidants and a-glucosidase and tyrosinase inhibitors, and thus, relevant for different commercial segments like the pharmaceutical, cosmetic and/or food industries.FCT - Foundation for Science and Technology [CCMAR/Multi/04326/2013]; Portuguese National Budget; FCT [IF/00049/2012, SFRH/BD/94407/2013]; Research Foundation - Flanders (FWO) [12M8315N]info:eu-repo/semantics/publishedVersio