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In this study, we aimed to determine the effects of tillage systems (TS) on wheat grain yield, yield components, and physicochemical properties under rain-fed conditions over 2 years (2018–2019) in the Kuhdasht region in southwestern Iran. The tillage treatments were a combination of conventional tillage (CT), reduced tillage (RT), and no tillage (NT) systems based on randomized complete blocks with three replications. Wheat grain yields and yield components, physical properties (geometrical dimensions, sphericity, thousand-grain mass (TGM), water absorption (WA), volume (V), and density), chemical composition (protein content (PC), water content (WC), fiber content (FC), ash content (AC)), surface color parameters (L*, a*, and b*), and correlations among measured parameters were assessed. The results showed that wheat grain yield and yield components were significantly affected by TS (p < 0.01). The wheat grain yield was higher under CT (2.72 t ha−1), with a significant difference between RT (1.76 t ha−1) and NT (1.20 t ha−1). The highest TGM (49.19 g) was achieved under CT, followed by RT (43.41 g), with the lowest (39.17 g) in the NT system. TS had significant effects on certain physical properties of wheat grains (p < 0.01). Wheat grain, size, shape, and mass values were higher under CT than RT and NT systems in all experimental years. CT resulted in the highest WA, while the lowest resulted from NT. TS had no significant influence on the density of the grains. TS had significant effects on PC, AC, and WC values of grains at the 1% probability level and on FC at the 5% level. Higher PC values were obtained under the CT system (13.07%, against 11.90% and 10.67% for RT and NT, respectively) in all growing seasons. Additionally, the AC was significantly lower in the sample grains under RT (2.38%) and NT (2.43%) than in those from CT (2.56). The FC was higher under NT (15.73%) than RT (13.73) and CT (13.71%). The grain WC was significantly higher under NT (7.57%) than RT (6.79%) and CT (7.0%). The TS significantly affected the surface color parameters of grains, while the L* (lightness) and b* (yellowness) values of grains under CT were higher than in RT and NT.

This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.

Although climate-smart agriculture can simultaneously decline greenhouse gas emissions, increase the adaptive capacity of farmers and improve food security under climate change, constraints and drivers of scaling up are not entirely addressed in developing countries. This qualitative case study was conducted on both strawberry growers and agricultural experts to explore the perceived causes, evidence and impacts of climate change, adaptation strategies used by farmers, and constraints and drivers of climate-smart agriculture development on the strawberry farms in Kurdistan province, Western Iran. Findings indicated that the causes of climate change could be divided into anthropogenic and natural forces. Decreased precipitation, increased temperature, dust storms, greenhouse gases, forest fires, spring frosts, severe hail, floods and droughts comprised the most notable climate change evidence in the region. Both groups confirmed the impacts of climate change on the reduction in strawberry yield, increasing the perishability of the fruits, poverty, migration and other social problems. Adaptation strategies used by farmers are classified into technical–agricultural, water conservation, farm smartening, and institutional adaptation practices. However, poverty, the shortage of strawberry-processing industries, insufficient financial support, the presence of intermediaries and brokers, traditional cultivation, difficulties in shipping strawberry crops to the market, the lack of storage facilities and equipment and the export terminal along with the mistrust of strawberry growers in the agricultural organization hinder climate-smart agriculture development in the study area. Finally, several drivers were proposed, which were considered the basis for providing practical suggestions for planning and policy making for climate-smart agriculture development in strawberry farms.

Sufficient production, consistent food supply, and environmental protection in urban +settings are major global concerns for future sustainable cities. Currently, sustainable food supply is under intense pressure due to exponential population growth, expanding urban dwellings, climate change, and limited natural resources. The recent novel coronavirus 2019 (COVID-19) pandemic crisis has impacted sustainable fresh food supply, and has disrupted the food supply chain and prices significantly. Under these circumstances, urban horticulture and crop cultivation have emerged as potential ways to expand to new locations through urban green infrastructure. Therefore, the objective of this study is to review the salient features of contemporary urban horticulture, in addition to illustrating traditional and innovative developments occurring in urban environments. Current urban cropping systems, such as home gardening, community gardens, edible landscape, and indoor planting systems, can be enhanced with new techniques, such as vertical gardening, hydroponics, aeroponics, aquaponics, and rooftop gardening. These modern techniques are ecofriendly, energy- saving, and promise food security through steady supplies of fresh fruits and vegetables to urban neighborhoods. There is a need, in this modern era, to integrate information technology tools in urban horticulture, which could help in maintaining consistent food supply during (and after) a pandemic, as well as make agriculture more sustainable.

Cover crops (CCs) are a promising strategy for maintaining and enhancing agroecosystem sustainability, yet CCs’ effects on the subsequent crop yield are highly variable. To quantitatively synthesize the effects of CCs on subsequent crop yield, a meta-analysis of 672 observations collected from 63 recent studies (2015 to 2021) in temperate climates was conducted. Legume CC species increased subsequent crop yield significantly more than grass (by 14%), nonlegume broadleaves (by 7%), and mixtures (by 2%). Incorporation of CC residue into soil increased crop yield by approx. 15% compared to leaving the CC residue on the soil surface. Relative to the no-CC control, the adoption of grass and legume CC species in non-organic vegetable cropping systems enhanced crop yield by 14% and 19%, respectively. Likewise, crop yield with legume CCs in coarse and medium textured soil, and under high precipitation conditions (>700 mm), was significantly greater than the no-CC control by 18%, 4%, and 11%, respectively. Cover crops significantly increased vegetable crop yields and decreased the silage corn yield; however, grain corn, soybean, and winter wheat yield did not decrease with CC. Adoption of CC in no-tillage and plow tillage systems contributed to an increase in crop yield compared to the no-CC control. Our meta-analysis highlights that crop yield response to CC might become more robust when pedo-climatic conditions and agronomic factors are considered.

Using survey information of 150 randomly selected households across 21 villages of the forest-rich district of Swat, Pakistan, this study assessed households’ decision-making behaviors in depending on income from nearby forested land using socio-economic attributes. The evidence from the study may aid in making the existing policies be better targeted toward families that depend on the forest for income. Descriptive statistics and econometric techniques such as logit and tobit were used to analyze the data. Respondent households obtained the highest share of their income from off-farm activities (37%) and least from forest activities (16%). Fuelwood constitutes the biggest share (66%) of forest income, followed by medical plants (20%) and fodder (13%). We found that households with more physical assets, more family members working in off-farm jobs, and households earning more income from off-farm jobs were significantly and negatively associated with households’ decision to depend on forest income and total income obtained. We also found that households with less distance to the market and membership to joint forest management committees (JFMCs) were significantly and negatively associated with households’ total income obtained. However, household size was significantly and positively related to households’ decision of forest dependency. The study recommends the creation of off-farm opportunities and inclusion of local people in the management of forests through establishment of JFMCs, particularly for large and poor families.

Dwindling water resources have drawn global attention to the reuse of treated wastewater (TWW) for irrigation. However, the impact of continuous TWW applications on soil quality and the proper quantification and monitoring frameworks have not been well-understood. This study aims to provides an insight into the impact of flood irrigation of urban TWW on soil nutritional-chemical attributes and the potential application of multiple soil quality indices for a corn cropping system. To achieve that goal, we pursued the Total Data Set (TDS) and Minimum Data Set (MDS) approaches, as well as the Integrated Quality Index (IQI) and Nemoro Quality Index (NQI) models. A total of 17 soil nutritional-chemical indicators (0–50 cm depths) were determined for the soils irrigated with TWW (five sites) and well water (one site as control) in West Azerbaijan province in northwestern Iran. Results revealed a significant difference in the majority of soil nutritional-chemical attributes, IQI-TDS, NQI-TDS, IQI-MDS, NQI-MDS, and corn yield between the TWW-irrigated and well-irrigated soils. Irrigation with TWW resulted in a significant increase in the amount of organic matter and cation exchange capacity by 9–17% and 17–26%, respectively, macronutrients (N, P, K, Ca, and Mg) by 22–164%, and the majority of trace metals (Fe, Mn, Zn, and Cu) by 17–175%, suggesting an improvement in soil nutrients and an increase in productivity. Comparing to the soil in control sites, the TWW irrigation caused a notable increase in the values of IQI-TDS, NQI-TDS, IQI-MDS, and NQI-MDS models ranging 14.6–29.5%, 19.1–25.5%, 21.7–33.3%, and 18.4–23.7%, respectively. This implies that soil quality was ameliorated to a significant extent with TWW irrigation. These improvements resulted in a remarkable increase in corn yield ranging from 12.5% to 28.1%. The regression equations revealed that up to 78%, 47%, 72%, and 36% of the variance in the IQI-TDS, NQI-TDS, IQI-MDS, and NQI-MDS models, respectively, could be captured by corn yield. The results of the regression and correlation analyses showed that the IQI-MDS model was more accurate than the other models in assessing soil quality and predicting crop yield. These findings may be an effective and practical tool for policy making, implementation, and management of soil irrigated with TWW.