Exploring the poverty-reduction benefits of agricultural machinery socialization services in China: Implications for the sustainable development of farmers.

Sustainable development is crucial for alleviating poverty among farmers. In this study, we examined the impact, and the mechanism underlying this impact, of the adoption of agricultural machinery services by farmers on their relative poverty from a multidimensional poverty perspective by employing the logit and ordered logit models and the Karlson-Holm-Breen (KHB) method. These results indicate that adopting agricultural machinery services can significantly reduce the probability of relative poverty among farmers, thereby expediting the sustainability of rural development. However, this poverty-reduction effect varies based on age and sex. The adoption of agricultural machinery services mainly reduces poverty by increasing farmers' human capital. Training in employment skills and non-agricultural work experience are the main transmission mechanisms. Therefore, the socialization of agricultural machinery services can be used as an effective policy tool to reduce relative poverty in developing countries, promote sustained improvements in farmers' incomes, and achieve sustainability in rural development.

Research Progress on the Wear Resistance of Key Components in Agricultural Machinery.

Agricultural mechanization is crucial in enhancing production efficiency, alleviating labor demands, reducing costs, improving agricultural product quality, and promoting sustainable development. However, wear and tear are inevitable when using agricultural machinery. The failure of critical wear-resistant parts is responsible for over 50% of rural machinery breakdowns. For instance, a domestic combine harvester typically only operates trouble-free for 20 to 30 h, and the service life of a rotary plow knife is approximately 80 h. Investigating the wear performance of key farm machinery components reinforces machinery design and maintenance strategies, extends machinery lifespans, enhances agricultural production efficiency, and advances agrarian sustainability. This paper provides a comprehensive overview of the latest research on the wear resistance of crucial agricultural machinery components. It delves into the factors influencing the wear resistance of these components and explores current effective measures to address wear-related issues. Additionally, it also summarizes the challenges and opportunities in researching the wear performance of key components in agricultural machinery and future development directions.

The role of agricultural machinery in improving green grain productivity in China: Towards trans-regional operation and low-carbon practices.

Agricultural machinery is effective in improving food production and labor productivity, but it also raises environmental concerns. The impacts of agricultural machinery on the green total factor productivity (GTFP) of grain in China are still under debate in the scientific literature. This study proposed an integrated framework for confronting this issue. The findings suggest that both agricultural mechanization and the GTFP of grain demonstrate a consistent upward trend with moderate fluctuations between 2001 and 2019. By expanding the spatial pattern, there is a positive spatial correlation between them. In addition, we compared the results in three grain functional areas by using the spatial Durbin model (SDM). There were significantly positive spatial spillover effects in major grain-producing areas, which were attributed to the trans-regional operation of agricultural machinery and its carbon reduction effects on neighboring provinces. Notably, the direct effects in major grain-marketing areas and producing-marketing balance areas were significantly positive because agricultural machinery has played a critical role in filling the gap in local labor shortages in grain production. Accordingly, adaptive strategies including building the "Internet + agricultural machinery operation" platform, implementing the land consolidation suitable for machinery, and developing low-carbon agricultural machinery should be fully considered by Chinese policy-makers to promote mechanized agriculture and a low-carbon economy. The findings of this study can help us better understand the role of agricultural machinery in improving green grain productivity in China and thus have significance for the modern and green transformation of agricultural production systems.

Real-World Emission Characteristics of Full-Volatility Organics Originating from Nonroad Agricultural Machinery during Agricultural Activities.

Nonroad agricultural machinery (NRAM) emissions constitute a significant source of air pollution in China. Full-volatility organics originating from 19 machines under 6 agricultural activities were measured synchronously. The diesel-based emission factors (EFs) for full-volatility organics were 4.71 ± 2.78 g/kg fuel (average ± standard deviation), including 91.58 ± 8.42% volatile organic compounds (VOCs), 7.94 ± 8.16% intermediate-volatility organic compounds (IVOCs), 0.28 ± 0.20% semivolatile organic compounds (SVOCs), and 0.20 ± 0.16% low-volatility organic compounds (LVOCs). Full-volatility organic EFs were significantly reduced by stricter emission standards and were the highest under pesticide spraying activity. Our results also demonstrated that combustion efficiency was a potential factor influencing full-volatility organic emissions. Gas-particle partitioning in full-volatility organics could be affected by multiple factors. Furthermore, the estimated secondary organic aerosol formation potential based on measured full-volatility organics was 143.79 ± 216.80 mg/kg fuel and could be primarily attributed to higher-volatility-interval IVOCs (bin12-bin16 contributed 52.81 ± 11.58%). Finally, the estimated emissions of full-volatility organics from NRAM in China (2021) were 94.23 Gg. This study provides first-hand data on full-volatility organic EFs originating from NRAM to facilitate the improvement of emission inventories and atmospheric chemistry models.

Life cycle inventory data of agricultural tractors.

Life Cycle Assessments (LCA) of agricultural systems are performed using inventory data from several databases. The inventory data used for agricultural machinery and especially agricultural tractors in these databases are based on old data (from 2002 and not updated since) using trucks ("lorry") as a proxy for the manufacture of tractors. In consequences, they do not reflect the current technology used by farmers and do not allow comparison with new technologies in used in farms such as agricultural robots. The dataset proposed in this paper presents two updated Life Cycle Inventory (LCI) of an agricultural tractor. Data were collected based on the technical system of a tractor manufacturer, scientific and technical literature as well as expert opinion. Data on weight, composition, lifetime and maintenance hours of each tractor component as well as electronic parts, converter catalyst and lead battery are produced. The inventory is calculated based on the raw materials needed for the tractor manufacturing and maintenance over its lifetime as well as the energy and infrastructure needed for manufacturing. Calculations were made based on a tractor of 7300 kg with the following characteristics: 155 CV, 6 cylinders, four-wheel drive. The tractor modelled is representative of tractors from the same power category (i.e. between 100 and 199 CV and 70% of the annual sales in France). Two LCI are produced: a LCI for a 7200 h lifetime tractor, representative of an accounting depreciation, and a LCI for a 12000 h lifetime tractor, representative of the whole service life of the tractor (first use to final disposal). The functional unit is 1 kg of tractor (kg) or 1 piece (p) of tractor during its lifetime.

Real-world emission characteristics and inventory of volatile organic compounds originating from construction and agricultural machinery.

Volatile organic compound (VOC) emissions originating from nonroad mobile sources constitute an important but uncertain source of secondary organic aerosols (SOAs) and ozone (O3). In this study, we investigated the emission factors (EFs) of 120 individual VOC species for 40 machines via gas chromatography-mass spectrometry/flame ionization detection and high-performance liquid chromatography. The results showed that the diesel-based VOC EF for the tested machines was 4.18 ± 2.55 (average ± standard deviation) g/kg fuel, dominated by alkanes (38.20 % ± 18.08 %) and oxygenated VOCs (OVOCs; 30.94 % ± 15.71 %). The machine type, rated power, emission standards, and operating conditions affected the emissions of VOCs and their components, and this effect maybe mostly depends on the fuel combustion efficiency. The VOC species were primarily distributed in the C1-C2 and C4-C6 (based on the carbon number) and B4-B6 (based on the saturated vapor concentration) intervals. Furthermore, the estimated formation potential (FP) values of SOAs and O3 from VOCs were 21.02 ± 15.57 mg/kg fuel and 15.96 ± 11.87 g/kg fuel, respectively. VOC control based on the SOA formation potential (SOAFP) and ozone formation potential (OFP) could be more effective in the mitigation of fine particulate matter (PM2.5) and O3 pollution because the top 5 species ranked by percentage contribution accounted for 83.09 % ± 9.59 % and 51.78 % ± 14.38 % of the estimated SOAFP and OFP, respectively. Finally, the emission estimates showed that the VOC emissions originating from construction and agricultural machinery in China (2020) reached 64.05 and 95.24 Gg, respectively. We provide species-specific VOC EFs and detailed emission characteristics to facilitate a comprehensive understanding of gas emissions originating from nonroad mobile sources and an update of emission inventories and atmospheric chemistry models.

Heterogeneity and threshold in the effect of agricultural machinery on farmers' relative poverty.

Machinery is commonly used in the modernization of agricultural industries and is a pivotal way to eliminate poverty among farmers. However, there are still disputes regarding the effects of agricultural machinery on farmers' relative poverty. Neither the heterogeneity nor the thresholds in agricultural machinery-led poverty reduction efforts have been discussed in depth. To address those gaps, this study considers farmers' livelihood factors and resource (in)divisibility to investigate how agricultural machinery affects farmers' relative poverty as well as the heterogeneity of and thresholds in that influence. This study collected data from 1118 Chinese farming households. 2SLS-IVTobit regression results show that a 1% increase in the overall level of agricultural machinery leads to a 3.3% increase in farmers' income and a 0.523% decrease in their relative poverty. Furthermore, the three pathways of cost-saving, production efficiency, and labor allocation efficiency are identified as explaining 25.4%, 21.9%, and 21.3% of relative poverty reduction, respectively. The heterogeneity of these effects across different farming stages (i.e., plowing, sowing, and harvesting) is also examined, and the results show that plowing machinery has the largest effect. Then, a threshold analysis is conducted, which shows that farmers are influenced more when the scale of their farms surpasses the threshold of 1.12 hm2. Theoretically, this study establishes an integrated model that depicts how agricultural machinery affects farmers' relative poverty through production (in)divisibility. Practically, this study recommends additional investment in agricultural machinery (especially plowing machinery), farmland integration, and taking targeted measures to facilitate resource divisibility.

Impact of Agricultural Mechanization Level on Farmers' Health Status in Western China: Analysis Based on CHARLS Data.

Agricultural mechanization is an important component of agricultural modernization, as it contributes to the improvement of agricultural technology and the rapid transformation of agricultural development. However, research on the connection between agricultural mechanization and farmers' health status is scarce. Thus, using the 2018 China Health and Retirement Longitudinal Survey (CHARLS) data, this study explored how agricultural mechanization can affect farmers' health. OLS and 2SLS models were used for the study's analysis. Furthermore, we used a PSM model to check the robustness of our analysis. The findings showed that: (1) the current state of agricultural mechanization in western China harms the health of rural residents; (2) agricultural mechanization can mitigate the adverse effects on health by increasing farmers' living expenditure and improving their living environment; and (3) agricultural mechanization's effects on farmers' health are regionally and income-heterogeneous. Agricultural mechanization has a more significant impact on health in Tibetan areas and high-income regions. It has an almost minimal effect in non-Tibetan and low-income areas. This paper suggests approaches that can be used to encourage the rational development of agricultural mechanization and improve rural populations' health.

Emission and influences of non-road mobile sources on air quality in China, 2000-2019.

Non-road mobile sources (NRMS) are potential important contributors to air pollution in China. However, their extreme impact on air quality had been seldom studied. In this study, the emission inventory of NRMS in mainland China during 2000-2019 was established. Then, the validated WRF-CAMx-PSAT model was applied to simulate the contribution to the atmospheric PM2.5, NO3-, and NOx. Results showed that emissions increased rapidly since 2000 and reached a peak in 2014-2015, with an annual average change rate (AACR) of 8.7-10.0%; after then, the emissions were relatively stable (AACR, -1.4-1.5%). The modeling results indicated that NRMS has become a crucial contributor to the air quality in China: from 2000 to 2019, the contribution to PM2.5, NOx, and NO3- significantly increased by 131.1%, 43.9%, and 61.7%; and for NOx, the contribution ratio in 2019 reached 24.1%. Further analysis showed that the reduction (-0.8% and -0.5%) of the NOx and NO3- contribution ratios was much lower than that (-4.8%) of NOx emissions from 2015 to 2019, implying that the control of NRMS lagged behind the national overall pollution control level. The contribution ratio of agricultural machinery (AM) and construction machinery (CM) to PM2.5, NOx, NO3- in 2019 was 2.6%, 11.3%, 8.3% and 2.5%, 12.6%, 6.8%, respectively. Although the contribution was much lower, the contribution ratio of civil aircraft had the fastest growth (202-447%). Moreover, an interesting phenomenon was that AM and CM had opposite contribution sensitivity characteristics for air pollutants: CM had a higher Contribution Sensitivity Index (CSI) for primary pollutants (e.g., NOx), ∼1.1 times that of AM; while AM had a higher CSI for secondary pollutants (e.g., NO3-), ∼1.5 times that of CM. This work can provide a deeper understanding for the environmental impact of NRMS emissions and for the control strategy formulation of NRMS.

Assessing farmland suitability for agricultural machinery in land consolidation schemes in hilly terrain in China: A machine learning approach.

Identifying available farmland suitable for agricultural machinery is the most promising way of optimizing agricultural production and increasing agricultural mechanization. Farmland consolidation suitable for agricultural machinery (FCAM) is implemented as an effective tool for increasing sustainable production and mechanized agriculture. By using the machine learning approach, this study assesses the suitability of farmland for agricultural machinery in land consolidation schemes based on four parameters, i.e., natural resource endowment, accessibility of agricultural machinery, socioeconomic level, and ecological limitations. And based on "suitability" and "potential improvement in farmland productivity", we classified land into four zones: the priority consolidation zone, the moderate consolidation zone, the comprehensive consolidation zone, and the reserve consolidation zone. The results showed that most of the farmland (76.41%) was either basically or moderately suitable for FCAM. Although slope was often an indicator that land was suitable for agricultural machinery, other factors, such as the inferior accessibility of tractor roads, continuous depopulation, and ecological fragility, contributed greatly to reducing the overall suitability of land for FCAM. Moreover, it was estimated that the potential productivity of farmland would be increased by 720.8 kg/ha if FCAM were implemented. Four zones constituted a useful basis for determining the implementation sequence and differentiating strategies for FCAM schemes. Consequently, this zoning has been an effective solution for implementing FCAM schemes. However, the successful implementation of FCAM schemes, and the achievement a modern and sustainable agriculture system, will require some additional strategies, such as strengthening farmland ecosystem protection and promoting R&D into agricultural machinery suitable for hilly terrain, as well as more financial support.

Control and Measurement Systems Supporting the Production of Haylage in Baler-Wrapper Machines.

Baler-wrappers are machines designed to produce high-quality forage, in accordance with the requirements of sustainable agriculture. Their complicated structure, and significant loads occurring during operation, prompted the creation of systems for controlling the machines' processes and measuring the most important work parameters, in this work. The compaction control system is based on a signal from the force sensors. It allows for detection differences in the compression of the bale and additionally protects against overload. The method of measuring the swath size, with the use of a 3D camera, was presented. Scanning the surface and travelled distance allows for estimating the volume of the collected material-making it possible to create yield maps (precision farming). It is also used to vary the dosage of ensilage agents, that control the fodder formation process, in relation to the moisture and temperature of the material. The paper also deals with the issue of measuring the weight of the bales-securing the machine against overload and collecting data for planning the bales' transport. The machine, equipped with the above-mentioned systems, allows for safer and more efficient work, and provides information about the state of the crop in relation to a geographical position, which allows for further inferences.

Multi-type emission factors quantification of black carbon from agricultural machinery based on the whole tillage processes in China.

Black carbon (BC), as one of the short-lived climate pollutants, is becoming more prominent contribution from non-road mobile source, especially for agricultural machinery (AM) in China. However, the understanding of BC emissions from AM is still not clear, and the BC emission factors (EFs) are also limited. In this study, we conducted real-world measurements on twenty AM to investigate the instantaneous BC emission characteristics and quantify BC EFs under the whole tillage processes. We find the instantaneous BC emissions and fuel consumptions are obvious differences and present good synchronization under different tillage processes. Multi-type (CO2-, fuel-, distance-, time-, and area-based) EFs of BC are developed, which are significantly affected by different tillage processes and emission standards of the used AM. While AM conducting rotary tillage, ploughing, harvest corn and harvest wheat on the same area of land, total BC emissions by using the China III emission standard AM will be reduced by 56%, 36%, 88%, and 87% than those by using China II emission standard AM, respectively. Furthermore, for corn and wheat production under the whole tillage processes, BC EFs are 16.90 (6.03-39.12) g/hm2 and 18.18 (5.91-38.69) g/hm2, CO2 EFs are 112.64 (72.07-195.98) g/hm2 and 103.72 (71.47-167.02) g/hm2, respectively. We estimate the BC and CO2 emissions from wheat and corn productions based on the average area-based EFs. The large fluctuation ranges of BC and CO2 emissions in different tillage processes and the whole processes can reflect that the use of AM in China is uneven. It also indicates that there is a large space for BC and CO2 emission reduction and optimization. Therefore, more attention should be paid to the control of BC and CO2 emissions from AM. We believe that the recommended multi-type EFs are applicable for the quantification of BC emissions from AM in China and other countries.

Tasseled Crop Rows Detection Based on Micro-Region of Interest and Logarithmic Transformation.

Machine vision-based navigation in the maize field is significant for intelligent agriculture. Therefore, precision detection of the tasseled crop rows for navigation of agricultural machinery with an accurate and fast method remains an open question. In this article, we propose a new crop rows detection method at the tasseling stage of maize fields for agrarian machinery navigation. The whole work is achieved mainly through image augment and feature point extraction by micro-region of interest (micro-ROI). In the proposed method, we first augment the distinction between the tassels and background by the logarithmic transformation in RGB color space, and then the image is transformed to hue-saturation-value (HSV) space to extract the tassels. Second, the ROI is approximately selected and updated using the bounding box until the multiple-region of interest (multi-ROI) is determined. We further propose a feature points extraction method based on micro-ROI and the feature points are used to calculate the crop rows detection lines. Finally, the bisector of the acute angle formed by the two detection lines is used as the field navigation line. The experimental results show that the algorithm proposed has good robustness and can accurately detect crop rows. Compared with other existing methods, our method's accuracy and real-time performance have improved by about 5 and 62.3%, respectively, which can meet the accuracy and real-time requirements of agricultural vehicles' navigation in maize fields.

A robust H∞-based steering assistance system for the wheeled tractor.

Agricultural machine automatic navigation poses great challenge to the precise agricultural technology system nowadays. To this end, this paper proposes a novel steering assistance system (SAS) to assist drivers in the path-tracking. First, the driver steering model is investigated through the driver simulator tests. Combining the wheeled tractor kinematics model, a driver-vehicle model is developed. Then, a polytopic linear parameter-varying (LPV) system is adopted to describe the uncertainties, including time-varying driver model parameters and velocity, in the model, based on which an output-feedback robust controller is developed to ensure robust stability within the polytope space. Moreover, a regional pole placement method is adopted to improve the transient performance of the system. Finally, driver-in-the-loop and field tests conducted to value the controller. The results show the effectiveness of the proposed method to improve the path-tracking performance for the agricultural machine navigation, while reducing the physical and mental workload of drivers. This control method is expected to be a paradigm for the precise navigation system of the agricultural machinery.

The combined effect of climatic factors and technical advancement on yield of sugarcane by using ARDL approach: evidence from Pakistan.

Sugarcane is one of the most important crops in the world and has a major influence on environmental concerns. This study aims to examine the association between sugarcane crop yield, climate change factors, and technical advancement using time series data for the period of 1989 to 2015 in Pakistan. An autoregressive distributed lag (ARDL) model and descriptive statistics analysis were employed in this study. The outcomes of the bound F-test for co-integration confirmed that there is a long-run and short-run equilibrium among sugarcane crop yield, temperature, rainfall, fertilizer use, and agricultural machinery. The results of long-run estimate that the coefficient of area, rainfall, and fertilizer use have significantly positive impacts on sugarcane crop yield. The coefficient of temperature had positive and non-significant while agricultural machinery had negative and statistically significant relationship with sugarcane crop yield. In the short-run estimates, the coefficient of area, rainfall, and fertilizer use have statistically positive impact, temperature had non-significant impact, and agricultural machinery had significantly negative impact on the yield of sugarcane crop. In addition, both CUSUM and CUSUMsq test results confirmed the goodness of fit of this model. The outcomes of our study suggest that climate change has negative impact on the yield of sugarcane. Based on the study findings, the Government requires to take effective measures for constructive policy-making and identification of environmental threats in Pakistan. Large-scale mechanical activities and rapid growing may be useful initiatives for raising the yield of sugarcane. Furthermore, technical advancement needs to be improved because it plays a vital role in increasing the yield of sugarcane and other major crops.

A computer system supporting agricultural machinery and farm tractor purchase decisions.

Operational research, i.e. searching for optimal solutions in a situation of uncertainty and risk, can also be used to support decisions to purchase expensive agricultural machinery. Although Polish farmers receive subsidies from the EU, it does not mean they do not need to make well-thought-out purchases, because wrong purchase decisions will have long-term consequences while using machinery. The article presents the results of the IFOP - the system which has been available on the Internet for several years. It collects data on farming machinery and vehicles based on users' voluntary but subjective opinions. The authors of this article developed an original multi-criteria method of evaluating the quality of these specific products, which enabled them to make relevant rankings of brands. It is an algorithmic-heuristic method, which uses pairwise comparison tools to determine the significance ratios of the criteria. This article presents the results of the 1st and 2nd IFOP edition (Race Ranking), which included several dozen brands of tractors registered in Poland. More than fifty qualitative (Q) and non-qualitative (C) traits of farm tractors were taken into account. According to Polish farmers, Valtra - a Finnish brand of farm tractors, part of the AGCO concern, was the most versatile (Q = 4.39 and Q&C = 4.23). These tractors received the best opinions for their functionality, durability, ergonomics and safety.

Development of a high-resolution emission inventory of agricultural machinery with a novel methodology: A case study for Yangtze River Delta region.

Recent increased use of agricultural machines elevated the atmospheric pollutant emissions in the Yangtze River Delta (YRD) region in eastern China. Given the potentially large environmental and health impacts in busy seasons with enhanced machinery usage, it is important to accurately estimate the magnitude, spatial and temporal distributions of the emissions. We developed a novel method to estimate the real-world in-use agricultural machinery emissions, by combining satellite data, land and soil information, and in-house investigation. The machinery usage was determined based on the spatial distribution, growing and rotation pattern of the crops. The varied requirement of machinery power by heterogeneous soil texture, which was ignored in the previous studies, was considered in our methodology. The spatiotemporal pattern of machinery usage was determined based on the explored quantitative correlation between the local agricultural activity duration and the geographic location of the activity. A "grid-based" (30 × 30 m) inventory with daily emissions was then obtained, achieving significant improvement on spatial and temporal resolution. It substantially diminished the bias of previous inventories based on the machinery population or power installation census data. The emissions of NOX, PM2.5, CO and THC were estimated at 36300, 2000, 36900 and 8430 metric tons in YRD, with the majority contribution from Anhui and Jiangsu. Ten cities locating in northern and central Anhui and Jiangsu contributed the largest machinery emissions, accounting for 60% of the total emissions in YRD. Harvesting was found to have the largest emissions, followed by tilling and planting. Regarding the crops, the emissions from wheat and rice related machinery usage were the largest. In the busy seasons (spring and autumn), larger daily NOX and PM2.5 emissions were found from machinery than on-road vehicles in 42% of counties in Anhui and Jiangsu, highlighting the necessity of careful strategy making on controls of priority emission source.

[Emission Factors and Emission Inventory of Agricultural Machinery in Beijing Under Real-world Operation].

This study aimed to establish the emission factors and the emission inventory of agricultural machinery in Beijing in 2017 under real-world operation. The emissions of typical harvesters, tractors, agricultural transport vehicles, and farm construction machinery under real-world operation were tested by a portable emission measurement system. The results showed that different operation modes have a greater impact on the tailpipe emissions of agricultural machinery. The CO, NOx, HC, and PM emissions were relatively stable when the engine is idling and moving compared to when the excavator is performing actual work. According to the classification and emission standards of various types of machinery, a relatively perfect emission factor system of agricultural machinery in Beijing was established, which can provide reference and support for relevant research and management decisions. According to the emission factors of agricultural machinery and fuel consumption in Beijing, the emissions of CO, NOx, HC, and PM in 2017 were 2566.60, 1239.29, 563.08, and 538.32 t, respectively. The total pollutants of transport machinery, tractors, and combined harvester accounted for 98%, 95%, 95%, and 98% of the total concentrations of CO, NOx, HC, and PM, respectively. Therefore, transport machinery, tractor, and combined harvester should be the key control objects in the reduction of agricultural machinery pollution.

Estimation of energy flow and environmental impacts of quinoa cultivation through life cycle assessment methodology.

Quinoa is an adaptable plant that is rich in terms of nutritional properties. Currently, the promotion and cultivation of quinoa are expanding in Iran. The present study aimed to investigate the energy consumption of quinoa grain production and its environmental impacts through life cycle assessment. In this regard, in order to evaluate the environmental and energy indices, required data were collected from quinoa farmers in Isfahan. The high energy ratio (ER > 1) and positive net energy show that quinoa cultivation is efficient. Based on the results, irrigation water and nitrate fertilizer were identified as the major contributors to energy consumption. Based on the normalization method, the highest and lowest environmental impacts during the production process were related to the indices of marine aquatic ecotoxicity and ozone layer depletion, respectively. Results showed that in the global warming potential impact, 354 kg CO2eq. were emitted per production of 1 tonne of quinoa grain. Diesel fuel and nitrogen fertilizer had a significant effect on most environmental impacts. Proper management of chemical fertilizers and agricultural machinery are key factors for sustainable cultivation of quinoa.