Mortality of the cotton boll weevil in drip and sprinkler irrigated cotton crops

The cotton boll weevil, Anthonomus grandis grandis Boheman (Coleoptera: Curculionidae), is a key cotton crop pest in Brazil. Adverse climatic factors, such as high temperatures and low soil moisture, dehydrate oviposited cotton squares (bud flowers) on the ground and cause high mortality of its offspring within these plant structures. The objective of this research was to evaluate the mortality of the cotton boll weevil in drip and sprinkler irrigated cotton crops. The experimental design was in randomized blocks with two treatments: drip (T1) and sprinkler (T2, control) irrigated cotton crops with sixteen replications. Each parcel had one emergence cage, installed between two cotton rows per irrigation system, with 37 cotton squares with opened oviposition punctures and yellowish bracts, to capture adult cotton boll weevils. The average number of boll weevils that emerged from the cotton squares and the causes of mortality at different development stages were determined per treatment. Third-generation life tables of the boll weevil were prepared using the natural mortality data in drip and sprinkler irrigation treatments and plus actual, apparent and indispensable mortality rates and the lethality of each mortality cause. We conclude that the application of water directly to the root zone of the plants in a targeted manner, using the drip irrigation system, can cause high mortality of the cotton boll weevil immature stages inside cotton squares fallen on the ground. This is because the cotton squares fallen on the drier and hotter soil between the rows of drip-irrigated cotton dehydrates causing the boll weevils to die. This is important because it can reduce its population density of the pest and, consequently, the number of applications of chemical insecticides for its control. Thus, contributing to increase the viability of cotton production, mainly in areas of the Brazilian semiarid region where the cotton is cultivated in organic system.

An Application of IoT to revolutionize Agro Sector

In this paper, an IoT based automated water irrigation system is proposed. This system is used to increase the production rate of agriculture based on the internet of things (IoT) and cloud computing. Sensor technology has been developed and various kinds of sensor such as humidity, temperature, soil moisture sensor, and pH sensors are used to collect information about the condition of the soil. By using the advanced technologies, the farmers get benefitted for better production in agriculture. Keywords: Sprinkling, Smart Sensor Pouch, MSP 430, RS 485 Port, IoT, Cloud Computing

Study on Different Type of Irrigation System Suitable for South Region of Maharashtra

To study on different type of irrigation system suitable for south region of Maharashtra. We are attempting to find an irrigation system which would require less water and will be economical with higher yield of the crops for which it is installed. Irrigation is the artificial application of water to the soil through various systems of tubes, pumps, and sprays. Irrigation is usually used in areas where rainfall is irregular or dry times or drought is expected. There are many types of irrigation systems, in which water is supplied to the entire field uniformly Study of various types of irrigation method's such as surface irrigation, subsurface irrigation, drip irrigation and smart irrigation. We discussed about the different types of irrigation systems, there are several types of irrigation systems such as surface irrigation, sub-surface irrigation, drip irrigation, IOT, smart irrigation, sensor based irrigation in combination of traditional and modern type of irrigation. From above study we came to know the difference between automated irrigation system and manual irrigation system. We will know that automated irrigation system gives higher yield of crops using less amount of water as compared to manual irrigation system in accordance to automated and manual. Our study is to compare our system with other irrigation systems in terms of economy and optimum water usage to provide maximum results. Keywords: Surface irrigation, Drip irrigation, Manual Irrigation system, automated irrigation system

A Deep Learning-Based Sensor Modeling for Smart Irrigation System

The use of Internet of things (IoT)-based physical sensors to perceive the environment is a prevalent and global approach. However, one major problem is the reliability of physical sensors’ nodes, which creates difficulty in a real-time system to identify whether the physical sensor is transmitting correct values or malfunctioning due to external disturbances affecting the system, such as noise. In this paper, the use of Long Short-Term Memory (LSTM)-based neural networks is proposed as an alternate approach to address this problem. The proposed solution is tested for a smart irrigation system, where a physical sensor is replaced by a neural sensor. The Smart Irrigation System (SIS) contains several physical sensors, which transmit temperature, humidity, and soil moisture data to calculate the transpiration in a particular field. The real-world values are taken from an agriculture field, located in a field of lemons near the Ghadap Sindh province of Pakistan. The LM35 sensor is used for temperature, DHT-22 for humidity, and we designed a customized sensor in our lab for the acquisition of moisture values. The results of the experiment show that the proposed deep learning-based neural sensor predicts the real-time values with high accuracy, especially the temperature values. The humidity and moisture values are also in an acceptable range. Our results highlight the possibility of using a neural network, referred to as a neural sensor here, to complement the functioning of a physical sensor deployed in an agriculture field in order to make smart irrigation systems more reliable.

Implementation of a LoRaWAN Based Smart Agriculture Decision Support System for Optimum Crop Yield

A majority of the population of developing countries is associated with agriculture directly or indirectly. The liaison of engineering technology and Sustainable Development Goals (SDGs) can build a bridge for farmers to enhance their skills regarding advancements through future generation agriculture trends. The next-generation trends include better soil preparation, intelligent irrigation systems, advanced methods of crop nutrient inspection, smart fertilizers applications, and multi-cropping practices. This work proposes a smart Decision Support System (DSS) that acquires the input parameters based on real-time monitoring to optimize the yield that realizes sustainability by improving per hectare production and lessening water seepage wastage in agribusiness. The proposed model comprises three basic units including an intelligent sensor module, smart irrigation system and controlled fertilizer module. The system has integrated sensors, cloud employing decision support layers, and networking based DSS to recommend cautions for optimum sustainable yield. The intelligent sensors module contains a temperature and humidity sensor, NPK sensor, soil moisture sensor, soil conductivity sensor, and pH sensor to transmit the statistics to the cloud over the internet via Long Range (LoRa) using Serial Peripheral Interface (SPI) communication protocol. Moreover, an android application has been developed for real-time data monitoring according to GPS location and node information (accessed remotely). Furthermore, the DSS contemplates the accessible information from sensors, past patterns, monitoring climate trends and creating cautions required for sustainable fertilizer consumption. The presented results and comparison validate the novelty of the design as it embraces smart irrigation with smart control and smart decision-making based on accurate real-time field data. It is better than existing systems as it transmits the data over the LoRa that is an open-source communication with long-range transmission ability up to several kilometres. The sensor nodes helped in advancing the yield of crops, which resulted in achieving inclusive and sustainable economic goals.

Seasonal Yield Response Factor for Red Bombay Onion (Allium cepa L.) Variety in Arba Minch, Ethiopia

Background: For better water resources management in the areas of water shortage for crop production, deficit irrigation is very important. The understanding of the yield response factor to water deficit is crucial for efficient irrigation water management. Deficit irrigation for studying yield response factors is always practiced in the way of stressing the demand of the crops. The present study was done for the determination of the seasonal yield response factor of red Bombay onion variety under Arba Minch agro climate condition. Furthermore, it also examined the effect of furrow irrigation systems on the seasonal yield response factor. Methods: The experiment was conducted from August to November 2019. The experiment had six treatments, which were the combination of two furrow irrigation systems and three irrigation levels. Data were collected for soil moisture before and after each irrigation and bulb yield. The seasonal yield response factors were determined through simple linear regressions using SPSS software. Result: When considering the furrow irrigation system as a factor, the seasonal yield response factor for alternate furrow irrigation system was 1.18 while for paired row furrow irrigation system was 1.07. This red Bombay variety of onion clearly shows more sensitive to water stress for alternative furrow irrigation systems than paired row furrow irrigation systems. Therefore, in the area of water shortage paired row furrow irrigation system is better than alternate furrow irrigation system. The seasonal yield response factor as a wall for red Bombay onion variety in Arba Minch agro-ecological condition was 1.12. Therefore, the red Bombay onion variety in Arba Minch agroclimate condition was sensitive to water stress.

Hydraulic Performance Evaluation of Community-Managed Irrigation Scheme, North Shewa, Amhara, Ethiopia

Background: Irrigation scheme performance assessment is vital to evaluate the impacts of irrigation practices, to identify performance gaps and to improve system performances. However, irrigation performance assessment has not been carried out for Yelen irrigation scheme since its operation. Therefore, this study has been done with the purpose of identifying and understanding the current level of irrigation performance of Yelen irrigation scheme. The indicators of adequacy, efficiency, dependability, deficiency and equity were used to determine hydraulic performance. Primary data were collected through flow measurement using current meter and Parshall flume, transect walk, household surveys and group discussions, whereas Secondary data were collected from different sources. CROPWAT 8.0 model, SPSS, Microsoft excels and GIS software was used to analyze the data. Water delivery indicators were evaluated from the amounts of water which was actually applied and which should have been applied.Results: According to the results of the study, the value of adequacy, dependability, efficiency, deficiency and equity were found to be 0.84, 0.26, 0.93, 0.17and 0.34 respectively. Generally, the performance of the irrigation system is poor. It mainly resulted due to water scarcity, illegal water abstraction, sedimentation of canals and inadequate operation and maintenance provisions.Concussions: Therefore, adequate maintenance and suitable management approaches are required to improve the irrigation system performance. Under the water scarce situations, improving the performance of water delivery systems can offer an opportunity to realize field level water savings.

Determination of optimum irrigation scheduling for sorghum in Benna-Tsemay Woreda, Southern Ethiopia

Scarcity of water is the most severe constraint for sustainable development of agriculture in arid and semi-arid areas. Hence, novel irrigation water application systems need to be developed so that high crop yield and water productivity per unit of land can be increased. Thus, the field experiment was conducted with the objective of determining the effect of different soil moisture depletion levels on yield and water use efficiency of sorghum crop in Benna-Tsemay woreda at Enchete kebele, Southern Ethiopia. The experiment was conducted for two consecutive years (2019-2020). It was arranged in RCBD with three replications and treatment was rated for five levels of available soil moisture depletion (ASMD), where T1 = 60%, T2 = 80%, T3 = 100%, T4 = 120%, and T5 = 140% of ASMD. Analysis of variance has shown that yield and water use efficiency of sorghum crop was significantly (P < 0.05) affected by irrigation scheduling. As observed in this study, the most economically attractive and environmentally accepted for small scale farmers with tolerable cost of production and higher net benefit was obtained by application of T3 (100% ASMD) under conventional furrow irrigation system. Therefore, for this particular sorghum crop (teshale variety), it could be concluded that increased water saving and water productivity through irrigation at 100% ASMD under conventional furrow irrigation system can solve the problem of water shortage and would ensure the opportunity of further irrigation development in the study area and similar agro-ecology. Int. J. Agril. Res. Innov. Tech. 11(2): 95-102, Dec 2021