Toxicity evaluation of collagen hydrolysates from chrome shavings and their potential use in the preparation of amino acid fertilizer for crop growth

Resource utilization of chrome shavings (CS) has attracted a lot of attention from scientists and technologists in leather industry. Especially, the collagen hydrolysates extracted from CS are expected to find potential application values in agricultural field. However, there is no biotoxicity analysis of collagen hydrolysates from CS. Herein, the collagen hydrolysates with different molecular weights were produced from CS by three hydrolysis dechroming methods including alkaline hydrolysis, enzymatic hydrolysis and alkaline-enzymatic synergistic hydrolysis, and the optimal hydrolysis process of CS was designed and conducted. To evaluate their toxicity, the three collagen hydrolysates were formulated into a nutrient solution for zebrafish development. The obtained results indicated that the hydrolysates with low concentrations (less than 0.6 mg/mL) were safe and could promote the development for zebrafish embryos. Furthermore, the three collagen hydrolysates were utilized as organic nitrogen sources and formulated into amino acid water-soluble fertilizers (AAWSF) including alkaline type fertilizer (OH), enzymatic type fertilizer (M) and alkaline-enzymatic type fertilizer (OH–M) for the early soilless seeding cultivation of wheat, soybean and rapeseed. It is worth mentioning that the chromium contents in the prepared AAWSF were less than 10 mg/kg, which is far less than the limit value in the standard (China, 50 mg/kg). The growth and development of seedlings (germination rate, plant height, fresh weight of leaves, soluble sugar content and chlorophyll content) were investigated. The corresponding results showed that the growth of seedlings watered with AAWSF was better compared with the other treatments, and the OH–M fertilizer had the best promoting effect on the seedlings growth and development, followed by the M and OH fertilizers. The safe toxicity assessment of the collagen hydrolysates will expand their application scope, and the use of collagen hydrolysates extracted from CS for seedlings growth also provides an effective and reasonable way to deal with the chromium-containing leather solid waste, which is an effective way to realize its resource utilization. Graphical Abstract

How to Promote the Agricultural Company Through Environmental Social Responsibility to Achieve Sustainable Production?

This survey employs the multilevel growth curve model to demonstrate how to promote the development of the company’s environmental innovation in agricultural companies specializing in the agricultural production and export of agricultural products to achieve sustainable production through environmental social responsibility and environmental engagement according to the engagement theory. The empirical data are collected 30 chief executive officers and their 90 supervisors of top management teams (TMTs) of Taiwanese agricultural companies in 2 months. The empirical results demonstrate that environmental social responsibility significantly influences the top management teams’ environmental engagement development, which in turn significantly influences the agricultural company’s environmental innovation. These empirical results can not only promote the sustainable production literature in the agricultural field but also help these agricultural companies implement environmental innovation to realize sustainable production of agricultural exports.

Landscape Characteristics Affecting Small Mammal Occurrence in Heterogeneous Olive Grove Agro-Ecosystems

Understanding how small mammals (SM) are associated with environmental characteristics in olive groves is important to identify potential threats to agriculture and assess the overall conservation value and functioning of agro-ecosystems. Here, we provide first insights on this topic applied to traditional olive groves in northeast (NE) Portugal by assessing the landscape attributes that determine SM occurrence, focusing on one species of conservation concern (Microtus cabrerae Thomas 1906) and one species often perceived as a potential pest of olives (Microtus lusitanicus Gerbe 1879). Based on SM genetic non-invasive sampling in 51 olive groves and surrounding habitats, we identified seven rodent species and one insectivore. Occupancy modelling indicated that SM were generally less detected within olive groves than in surrounding habitats. The vulnerable M. cabrerae reached a mean occupancy (95% CI) of 0.77 (0.61–0.87), while M. lusitanicus stood at 0.37 (0.24–0.52). M. cabrerae was more likely to occur in land mosaics with high density of agricultural field edges, while M. lusitanicus was more associated with high density of pastureland patches. Overall, our study suggests that the complex structure and spatial heterogeneity of traditionally managed olive grove agro-ecosystems may favor the occurrence of species-rich SM communities, possibly including well-established populations of species of conservation importance, while keeping potential pest species at relatively low occupancy rates.

Toward the Next Generation of Digitalization in Agriculture Based on Digital Twin Paradigm

Digitalization has impacted agricultural and food production systems, and makes application of technologies and advanced data processing techniques in agricultural field possible. Digital farming aims to use available information from agricultural assets to solve several existing challenges for addressing food security, climate protection, and resource management. However, the agricultural sector is complex, dynamic, and requires sophisticated management systems. The digital approaches are expected to provide more optimization and further decision-making supports. Digital twin in agriculture is a virtual representation of a farm with great potential for enhancing productivity and efficiency while declining energy usage and losses. This review describes the state-of-the-art of digital twin concepts along with different digital technologies and techniques in agricultural contexts. It presents a general framework of digital twins in soil, irrigation, robotics, farm machineries, and food post-harvest processing in agricultural field. Data recording, modeling including artificial intelligence, big data, simulation, analysis, prediction, and communication aspects (e.g., Internet of Things, wireless technologies) of digital twin in agriculture are discussed. Digital twin systems can support farmers as a next generation of digitalization paradigm by continuous and real-time monitoring of physical world (farm) and updating the state of virtual world.

Computer Vision and IoT based Automated Hydroponic Farms in Urban Areas- A Soilless Cultivation

As the populace increments and characteristic assets decline, the capacity to serve humankind with an adequate measure of nourishment turns out to be progressively troublesome. The measure of rural land diminishes relatively to the expanding populace, along these lines the measure of nourishment delivered will diminish fundamentally, and will be lacking to serve the developing populace. The universal strategies for cultivating won't do the trick sooner. Thus, using modern technology and resources, a method of efficient farming must be introduced and employed in the agricultural field. This report introduces a method of efficient farming using hydroponics. The system is automated and uses sensor data to make decisions to benefit the crops being grown. The system runs on Raspberry PI and Arduino, and utilizes OpenCV. With our system we hope to solve the potential food crisis and give everyone access to fresh produce all year round.

IMPACT OF ORGANIC AND BIOFERTILIZER APPLICATION ON YIELD AND YIELD ATTRIBUTING CHARACTERS IN LINSEED (Linum usitatissimum L.)

Linseed (Linum usitatissimum L.) belongs to family Linaceae, is the second most important rabi oilseed crop and stands next to rapeseed – mustard in area of cultivation and seed production in India. Flaxseed is grown as either oil crop or a fibre crop with fibre linen derived from the stem of fibre varieties and oil from the seed of linseed varieties. Several studies have been conducted on effect of fertilizers on growth and yield of linseed (Linum usitatissimum L.) varieties which revealed their enhancing role on the quality and quantity of flax cultivars. In this regards, a present study was planned and conducted during the Rabi season of 2020-2021 in the Agricultural field of School of Agricultural Sciences, Shri Guru Ram Rai University, Dehradun, Uttarakhand, India to investigate the effect of organic and biofertilizer and integrated treatment on the growth and yield of Linseed. The findings were reported on important growth and yield attributed parameters such as plant height, total fresh weight, total yield, 1000- seed weight (g), and number of seed / capsules. The maximum growth and yield was reported in T6 treatment where biofertilizer have been applied in consortium form followed by vermicompost. Thus, the findings revealed that all the microbial strains in consortia used as bio fertilizers showed enhanced tern of vegetative growth of plants, total herbage yield and total seed yield at various stages. This may be due to sustained release of nutrients to supply the required elements in microbial strains. The biofertilizers exhibited beneficial effects on plant growth and development either through producing growth hormones like IAA, kinetin and gibberellins, synthesizing atmospheric nitrogen and its increased availability to greater protein synthesis as well as increasing Phosphorus availability to plant communities. Thus, it was concluded that the enhanced expression of yield and its related attributes will have beneficial impact in production of nutraceutical products of commercial importance.

Recognition of Image-Based Plant Leaf Diseases Using Deep Learning Classification Models

Plant diseases are spread by a variety of pests, weeds, and pathogens and may have a devastating effect on agriculture, if not handled in a timely manner. Farmers face umpteen challenges from a proper water supply, untimely rain, storage facilities, and several plant diseases. Crops disease is the primary threat and it causes enormous loss to farmers in terms of production and finance. Identifying the disease from several hectares of agricultural land is a very difficult practice even with the presence of modern technology. Accurate and rapid illness prediction for early illness treatment to crops minimizes economical loss to the individual and further proves to be productive for healthy crops. Many studies use modern deep learning approaches to improve the accuracy and performance of object detection and identification systems. The suggested method notifies farmers of different agricultural illnesses, prompting them to take further essential precautions before the disease spreads to the whole agricultural field. The primary objective of this study is to detect the illnesses as soon as they begin to spread on the leaves of the plants. Super-Resolution Convolutional Neural Network (SRCNN) and Bicubic models are employed in the system to identify healthy and diseased leaves with an accuracy of 99.175 % and 99.156 % respectively.