CONSTRUCTION OF AN INDEX SYSTEM FOR SUSTAINABILITY ASSESSMENT IN SMALLHOLDER FARMING SYSTEMS

Highlights

● A five-step process for quantifying smallholder farming system sustainability is proposed.

● Definition of system boundary, functional unit, and indicators depends on research issues.

● Weighting, conversion, and aggregation methods tightly relates to the validity of assessment results.

Abstract

Smallholder farming systems are important for global food security, but these faces multiple environmental challenges hindering sustainable development. Although sustainable smallholder agriculture issues have been widely discussed and addressed by scientists globally, harmonized approaches in evaluating sustainability are still lacking. This paper proposes a five-step process for constructing a sustainability assessment method for smallholder farming systems, namely definition of system boundary, indicator selection, indicator weighting, indicator conversion, and indicator aggregation. The paper summarizes the state-of-art progresses in agricultural sustainability assessment at different stages, and systematically discussed the benefits and limitations of weighting and aggregation methods. Overall, this evaluation process should be useful by providing rational and comprehensive results for quantifying the sustainability of smallholder farming systems, and will contribute to practice by providing decision-makers with directions for improving sustainable strategies.

Graphical abstract

Keywords

indicator aggregation / multi-indicator / smallholder agriculture / sustainability assessment / weighting method

Introduction

Sprinkler irrigation can be defined as any irrigation system which distributes water as discrete droplets through the air. A water droplet may be considered as symmetric with respect to its axis of motions. In the absence of the wind, a droplet has a vertical trajectory. Two forces act upon a droplet in the air: (1) resistance, which opposes the relative movement of the droplet in the air and (2) gravity, which is in the vertical direction[ 1, 2]. The effect of resistance is to reduce the absolute magnitude of both velocity components. The effect of gravity is to increase the absolute magnitude of the vertical component when the droplet has a downward component and to decrease it when the drop has an upward component. Hence it follows that acceleration of a droplet is impossible as long as it has an upward velocity component. Acceleration of a droplet can never be followed by deceleration. Additionally, smaller droplets concentrate in the vicinity of the sprinkler and larger droplets always appear at the edge of the wetted radius. Accurate knowledge of droplet distribution for sprinklers is important. First, smaller droplets are subject to wind drift, distorting the application pattern. Second, larger droplets possess greater kinetic energy which is transferred to the soil surface causing particle dislodgement and ponding that may result in surface crusting and run-off[ 3- 6]. Thus, such information can be of practical importance in the design of sprinkler irrigation systems. Several articles have been published describing the droplet size distributions of specific types of sprinklers[ 7- 10]. Several models of sprinkler droplet flight trajectory have been developed in recent decades[ 11- 17] to investigate and predict sprinkler operating droplet characteristics. As reported by Hills and Gu, the effect of pressure on droplet diameter was more evident at large distances from the sprinkler[ 18]. The process of jet break-up into droplets is quite complex. At least two phases can be distinguished. In the first (no more than 1 or 2 m), the jet is quite compact, and in the second, the jet has nearly completely disintegrated, with a corresponding transitional phase[ 19].

Sprinkler irrigation systems operated at lower pressure have received attention in recent years because the energy loss increases. Today, the impact sprinkler is widely used around the world[ 20]. Two new prototype sprinklers, which were being adopted in the move toward low energy precision irrigation, may soon replace impact sprinkler[ 21– 24]. Dwomoh et al. have experimentally determined the droplet size distribution characteristics of one new type of sprinkler, complete fluidic sprinkler[ 25]. Zhu et al. compared the mean droplet diameter of different types of fluidic sprinkler[ 26]. However, information comparing droplet size and velocity distributions is still limited, and there have been only a few published reports. Three types of sprinkler, namely outside signal sprinkler (OS), fluidic sprinkler (FS) and impact sprinkler (IS), were compared in this study at different operating pressures. The aims of this study were to: (1) compare the three sprinkler types for droplet size and velocity distributions, and (2) develop a simple mathematical model to represent droplet distributions for OS and FS.

Materials and methods

Sprinklers

Three types of sprinkler head were specially fabricated for this study. OS and FS head designs were developed by the Research Center of Fluid Machinery Engineering and Technology (Jiangsu University, Zhenjiang, China). OS heads were specifically manufactured by mechanical machining as an experimental sample. FS heads were manufactured by Shanghai Watex Water-economizer Technology Co, Ltd., China. IS heads were from the Nelson Irrigation Co. Walla Walla, Washington, USA. The main differences among OS, FS, and IS were the nozzles: OS were equipped with 4.57 mm ellipse type nozzles (equivalent diameter), FS with 4.58 mm waist type nozzles, and IS with 4 mm circular nozzles. Additionally, the sprinkler jet formed 27°, 22°, 23° angles (with respect to the horizon) for OS, FS and IS, respectively. The OS and FS are gas-liquid fluidic sprinklers, whose driving moment is achieved by the flow reaction[ 21– 24].

Experimental design and procedure

The sprinklers were mounted on 1.5 m risers at 90° to the horizon. Four operating pressures were tested: 200, 250, 300 and 350 kPa, all within the manufacturer’s recommendations. Droplet sizes and velocities from the sprinklers were measured using a Thies Clima laser precipitation monitor (LPM) (Adolf Thies GmbH & Co. KG, Goettingen, Germany). The schematic diagram of experimental apparatus is shown in Fig. 1. Figure 2 shows the nominal measuring area of the laser beam. The wavelength of the laser diode was 785 nm and the droplet size and velocity measurement ranged from 0.16 to 8.00 mm and 0.20 to 20.00 m·s^-1, respectively. The sprinkler was allowed to rotate over the LPM at least five times to ensure a sufficient number of droplets passed through the measuring area. When a water droplet falls through the measuring area, the transmitted signal is reduced. The diameter of the droplet is calculated from the amplitude of the signal’s reduction, and velocity from its duration. In this study, droplet velocity measurements were collected at the edge of a wetted radius. The LPM was angled to allow the droplet to pass through the laser beam approximately normal to its face. The following standards were adopted in the design of the experimental setup and in the experiment itself: ASAE S.330.1 (1985), ASAE S.398.1 (1985) and ISO 7749-2 (1990), MOD GB/T 19795.2 (2005)[ 27- 29]. A minimum of three replications assessments were made for each pressure and data were averaged for use as the final experimental data.

Fig.1 Schematic diagram of experimental apparatus

Full size|PPT slide

Fig.2 Nominal measuring area of the laser beam

Full size|PPT slide

Droplet characterization experiments

Different experimental methods for evaluating droplet characteristics have been reported in the literature[ 30- 32]. The experiment was designed to characterize droplets from distances at the end of a wetted radius of the sprinklers at the four experimental pressures. At the observation point, droplets were characterized using the photographic method[ 33]. Specific details of the experimental methods were as described by King et al.[ 34]. The main problem with the laser method is that multiple drops, simultaneously moving through the laser beam, produce overlapped images which appear as a larger drops, causing overestimation of droplet sizes[ 35]. However, this problem was greatly reduced by the computer estimation of droplet velocity and subsequent rejection of droplets whose velocities were not consistent with the size class[ 36], thus reducing the probability of overlapped droplet images.

Basic droplet statistics: centrality and dispersion

Managing the large data sets obtained from the photographic detection required a statistical approach. While it is convenient to represent the sets by a reduced number of parameters, some traits of the droplet populations can be obscured by the choice of statistical parameters. The parameters for droplet diameter used in this work included the arithmetic mean diameter ( $\overline{d}$ , mm), the volumetric mean diameter (d_V, mm)[ 37], the median diameter (d₅₀, mm), the standard deviation (SD_D, mm) and the coefficient of variation (CV_D, %). They were determined by the equations:

(1) $\overline{d}={\displaystyle \frac{{\displaystyle \sum _{i=1}^n{m}_i{d}_i}}{{\displaystyle \sum _{i=1}^n{m}_i}}}$

(2) $$d_V={\displaystyle \frac{{\displaystyle \sum _{i=\mathrm{1}}^n{d}_i^{\mathrm{4}}}}{{\displaystyle \sum _{i=\mathrm{1}}^n{d}_i^{\mathrm{3}}}}}$$

(3) $S{D}_D=\sqrt{{\displaystyle \frac{1}{n-1}}{\displaystyle \sum _{i=1}^n{\left(d_i-\overline{d}\right)}^2}}$

(4) $C{V}_D=\left({\displaystyle \raisebox{1ex}{$S{D}_D$}\!\left/ \!\raisebox{-1ex}{$\overline{d}$}\right.}\right)\times \mathrm{100}\%$

where d_i is the diameter of each droplet in the set (mm), m_i is the droplet numbers of diameter d_i, and i is the number of droplets in the set (an ordinal number from 1 to n). Parameter $\overline{d}$ corresponds to the arithmetic mean droplet diameter. Parameter d_V corresponds to the volume-weighted average droplet diameter. Parameter d₅₀ can be obtained by sorting all droplets in the set by diameter and selecting the droplet diameter that represents 50% of the cumulative droplet frequency.

The arithmetic mean, standard deviation, and coefficient of variation were also used for droplet velocity ( $\overline{V}$ and SD_V, m·s^-1; CV_V, %).

Results and discussion

Regarding the results of this study it should be noted that operating conditions were controlled but differed slightly between each measurement (in particular, operating pressures varied slightly) and the number of droplets measured on each occasion was different.

Droplet size distributions

In this study, results using the photographic method were obtained at the edge of the wetted radius. The cumulative droplet diameter frequencies as a function of droplet diameter for the three sprinklers types are shown in Fig. 3. Recently, King et al. and Salvador et al. presented cumulative frequency charts and histograms based on photographic methods[ 33, 34]. These frequency charts describe the distribution of droplet diameters at each observation point. Both authors reported a large variability in droplet diameters, with trajectories similar to those reported in this article.

Fig.3 Measured droplet size distributions for each operating pressure. (a) OS (outside signal sprinkler); (b) FS (fluidic sprinkler); (c) IS (impact sprinkler).

Full size|PPT slide

As seen in Fig. 3, droplets had a wide range of diameters. The observation that very small droplets were found at the edge of a wetted radius supports the idea that not all droplets are formed at the nozzle. The mean OS droplet diameters varied from 0 to 3.4 mm. As a consequence, droplets under 1 mm had cumulative frequencies of 87%, 79%, 75% and 70%, under 2 mm of 94%, 93%, 95% and 96%, under 3 mm of 100%, 99%, 99%, and 100% at pressures of 200, 250, 300 and 350 kPa, respectively.

The mean FS droplet diameters varied from 0 to 3.5 mm. As a consequence, droplets under 1 mm had cumulative frequencies of 85%, 78%, 81% and 64%, under 2 mm of 90%, 89%, 90% and 93%, under 3 mm of 96%, 97%, 97% and 99% at pressures of 200, 250, 300 and 350 kPa, respectively. The mean IS droplet diameters varied from 0 to 4.0 mm. As a consequence, droplets under 1 mm had cumulative frequencies of 84%, 72%, 73% and 70%, under 2 mm of 91%, 94%, 94% and 94%, under 3 mm of 97%, 97%, 96% and 97% for a pressure of 200, 250, 300 and 350 kPa, respectively.

A summary of droplet sizes for 10%, 25%, 50%, 75% and 90% of the three sprinklers are given in Table 1.

Tab.1 Droplet sizes (mm) for 10, 25, 50, 75 and 90% (d10, d25, d50, d75, and d90, respectively) of three types of sprinkler

Sprinkler type	Operating pressure/kPa	d10	d25	d50	d75	d90
OS	200	0.04	0.11	0.24	0.37	1.69
	250	0.04	0.12	0.23	0.69	1.88
	300	0.05	0.13	0.23	1.02	1.82
	350	0.05	0.14	0.21	1.18	1.78
FS	200	0.04	0.12	0.37	0.44	2.05
	250	0.05	0.13	0.24	0.77	2.10
	300	0.05	0.13	0.23	0.48	2.02
	350	0.07	0.16	0.22	1.47	1.93
IS	200	0.05	0.12	0.44	0.44	1.92
	250	0.07	0.17	0.31	1.07	1.53
	300	0.06	0.14	0.25	1.07	1.82
	350	0.06	0.13	0.23	1.28	1.84

The comparison of droplet size distribution from the three types of sprinkler shows that OS and FS were similar to each other, as was expected given both are the gas-liquid fluidic sprinklers. Of the three sprinklers, OS had the narrowest droplet size range and the smallest maximum droplet diameter (about 3.4 mm), and IS had the widest droplet size range with a maximum value of about 4.0 mm. IS also had an approximately 0.5 mm larger droplet diameter than OS and FS. A new empirical droplet size equation for OS and FS was developed:

(5) $$CF=\mathrm{15.7}\ln (d_i)+\mathrm{75.3}$$

where CF is cumulative droplet diameter frequency, d_i is droplet diameter.

Overall, the expected values generated by this equation showed little deviation from the observed values overall. The average square of the correlation coefficient were 94% and 95% for OS and FS, respectively. The proposed equation is considered to be sufficiently accurate and simple for practical use for OS and FS.

Droplet velocity distributions

The relationship between velocity and distance was previously analyzed by Salvador et al. using the photographic method[ 33]. These authors reported velocities of 4 to 6 m·s^-1 for distances exceeding 10 m. Figure 4 shows the frequency distribution of droplet velocities for the three sprinklers at operating pressures of 200, 250, 300 and 350 kPa.

Fig.4 Droplet velocity frequency distribution for each operating pressure. (a) OS (outside signal sprinkler); (b) FS (fluidic sprinkler); (c) IS (impact sprinkler).

Full size|PPT slide

It is apparent that for various operating pressures, the mean OS droplet velocities ranged from 0 to 6.3 m·s^-1. As a consequence, droplets under 1 m·s^-1 had a frequency of 27%, 23%, 20% and 18%, under 3 m·s^-1 of 4%, 6%, 8% and 9%, under 5 m·s^-1 of 4%, 2%, 3% and 6% for a pressure of 200, 250, 300 and 350 kPa, respectively. The mean FS droplet velocities ranged from 0 to 6.3 m·s^-1. Droplets under 1 m·s^-1 had a frequency of 24%, 22%, 21% and 16%, under 3 m·s^-1 of 5%, 6%, 5% and 8%, under 5 m·s^-1 of 4%, 3%, 4% and 5% for a pressure of 200, 250, 300 and 350 kPa, respectively. The mean IS droplet velocities ranged from 0 to 6.8 m·s^-1. Droplets under 1 m·s^-1 had a freuqency of 23%, 17%, 17% and 21%, under 3 m·s^-1 of 5%, 6%, 6% and 5%, under 5 m·s^-1 of 3%, 3%, 2% and 3% for a pressure of 200, 250, 300 and 350 kPa, respectively.

This comparison shows that the maximum frequency value was obtained at velocities of 1 m·s^-1 for each combination. Velocities for OS and FS droplets were similar but not identical. Overall, IS tends to give greater velocities than OS or FS.

Droplet characterization: basic statistics

Table 2 show a number of statistical parameters for droplet diameter and velocity obtained for the three sprinklers at four operating pressures. Parameters include the arithmetic mean, standard deviation, coefficient of variation (for diameter, and velocity), the volumetric mean diameter, and the median diameter.

Tab.2 Statistical parameters for droplet diameter, and velocity obtained for different operating pressures

	Sprinkler	OS (outside signal sprinkler)				FS (fluidic sprinkler)				IS (impact sprinkler)
	Operation pressure/kPa	200	250	300	350	200	250	300	350	200	250	300	350
Diameter/mm	$\overline{d}$	0.84	0.75	0.67	0.56	0.89	0.76	0.70	0.68	0.78	0.76	0.73	0.64
$d_V$	2.70	2.24	2.21	2.18	2.78	2.74	2.16	1.48	2.99	2.39	2.21	1.92
	$d_{\mathrm{50}}$	0.24	0.23	0.23	0.21	0.37	0.24	0.23	0.22	0.44	0.31	0.25	0.23
SDD	0.87	0.67	0.75	0.81	1.01	0.99	0.94	0.85	0.92	0.63	0.77	0.77
	CVD	104	89	112	145	113	130	134	125	118	83	105	120
Velocity/(m·s-1)	$\overline{V}$	1.79	2.00	2.21	2.31	1.87	1.92	2.01	2.35	1.83	2.34	2.50	2.64
	SDV	1.41	1.34	1.36	1.36	1.47	1.29	1.43	1.33	1.35	1.31	2.10	2.25
	CVV	78	67	62	59	79	67	71	57	74	56	84	85

The mean droplet diameter (arithmetic, volumetric, and median) decreased and the mean velocity increased with an increase in operating pressure for all these three sprinklers.

For OS, the standard deviation of droplet diameter ranged from 0.67 to 0.87, with a mean of 0.78; the coefficients of diameter variation ranged from 89 to 145, with a mean of 112; the standard deviation of velocity ranged from 1.36 to 1.41, with a mean of 1.37; the coefficients of velocity variation ranged from 59 to 78, with a mean of 66. For FS, the standard deviation of droplet diameter ranged from 0.85 to 1.01, with a mean of 0.95; the coefficients of diameter variation ranged from 113 to 134, with a mean of 126; the standard deviation of velocity ranged from 1.29 to 1.47, with a mean of 1.38; the coefficients of velocity variation ranged from 57 to 79, with a mean of 68. For IS, the standard deviation of droplet diameter ranged from 0.63 to 0.92, with a mean of 0.77; the coefficients of diameter variation ranged from 83 to 120, with a mean of 107; the standard deviation of velocity ranged from 1.31 to 2.25, with a mean of 1.75; the coefficients of velocity variation ranged from 56 to 85, with a mean of 75.

Conclusions

The comparison of the droplet distributions for three types of sprinklers lead to the following important conclusions and outcomes:

(1) OS and FS droplet sizes and velocities were similar but not identical, because both are gas-liquid fluidic sprinklers. IS tends to give a 0.5 mm larger droplet diameter and 0.5 m·s^-1 greater velocities than OS and FS.

(2) The mean droplet diameter (arithmetic, volumetric, and median) decreased and the mean velocity increased in operating pressure for all three sprinklers.

(3) An new empirical equation (Equ. 5) for droplet size of the OS and the FS has been proposed. This equation provides sufficient accuracy and simplicity for routine application for OS and FS.

References

[1] ZhangX, YaoG, VishwakarmaS, DalinC, KomarekA M, KanterD R, DavisK F, PfeiferK, ZhaoJ, ZouT, D’OdoricoP, FolberthC, RodriguezF G, FanzoJ, RosaL, DennisonW, MusumbaM, HeymanA, DavidsonE A. Quantitative assessment of agricultural sustainability reveals divergent priorities among nations. One Earth, 2021, 4( 9): 1262–1277

[2] ChenX P, CuiZ L, VitousekP M, CassmanK G, MatsonP A, BaiJ S, MengQ F, HouP, YueS C, RömheldV, ZhangF S. Integrated soil-crop system management for food security. Proceedings of the National Academy of Sciences of the United States of America, 2011, 108( 16): 6399–6404

[3] YinY, ZhaoR, YangY, MengQ, YingH, CassmanK G, CongW, TianX, HeK, WangY, CuiZ, ChenX, ZhangF. A steady-state N balance approach for sustainable smallholder farming. Proceedings of the National Academy of Sciences of the United States of America, 2021, 118( 39): e2106576118

[4] KanterD R, BellA R, McdermidS S. Precision agriculture for smallholder nitrogen management. One Earth, 2019, 1( 3): 281–284

[5] ShenJ, ZhuQ, JiaoX, YingH, WangH, WenX, XuW, LiT, CongW, LiuX, HouY, CuiZ, OenemaO, DaviesW J, ZhangF. Agriculture green development: a model for China and the world. Frontiers of Agricultural Science and Engineering, 2020, 7( 1): 5–13

[6] YuX. Promoting agriculture green development to realize the great rejuvenation of the Chinese nation. Frontiers of Agricultural Science and Engineering, 2020, 7( 1): 112–113

[7] CuiZ, ZhangH, ChenX, ZhangC, MaW, HuangC, ZhangW, MiG, MiaoY, LiX, GaoQ, YangJ, WangZ, YeY, GuoS, LuJ, HuangJ, LvS, SunY, LiuY, PengX, RenJ, LiS, DengX, ShiX, ZhangQ, YangZ, TangL, WeiC, JiaL, ZhangJ, HeM, TongY, TangQ, ZhongX, LiuZ, CaoN, KouC, YingH, YinY, JiaoX, ZhangQ, FanM, JiangR, ZhangF, DouZ. Pursuing sustainable productivity with millions of smallholder farmers. Nature, 2018, 555( 7696): 363–366

[8] MortonJ F. The impact of climate change on smallholder and subsistence agriculture. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104( 50): 19680–19685

[9] Food and Agriculture Organization of the United Nations (FAO). World food and agriculture-statistical yearbook 2020. Rome: FAO , 2020. Available at FAO website on June 22, 2022

[10] ZhangW, CaoG, LiX, ZhangH, WangC, LiuQ, ChenX, CuiZ, ShenJ, JiangR, MiG, MiaoY, ZhangF, DouZ. Closing yield gaps in China by empowering smallholder farmers. Nature, 2016, 537( 7622): 671–674

[11] CuiJ, SuiP, WrightD L, WangD, SunB, RanM, ShenY, LiC, ChenY. Carbon emission of maize-based cropping systems in the North China Plain. Journal of Cleaner Production, 2019, 213 : 300–308

[12] ZhangQ, ChuY, XueY, YingH, ChenX, ZhaoY, MaW, MaL, ZhangJ, YinY, CuiZ. Outlook of China’s agriculture transforming from smallholder operation to sustainable production. Global Food Security, 2020, 26 : 100444

[13] PooreJ, NemecekT. Reducing food’s environmental impacts through producers and consumers. Science, 2018, 360( 6392): 987–992

[14] Król-BadziakA, Pishgar-KomlehS H, RozakisS, KsiężakJ. Environmental and socio-economic performance of different tillage systems in maize grain production: application of life cycle assessment and multi-criteria decision making. Journal of Cleaner Production, 2021, 278 : 123792

[15] ChaudharyA, GustafsonD, MathysA. Multi-indicator sustainability assessment of global food systems. Nature Communications, 2018, 9( 1): 848

[16] ChenY, LiuC, ChenJ, HuN, ZhuL. Evaluation on environmental consequences and sustainability of three rice-based rotation systems in Quanjiao, China by an integrated analysis of life cycle, emergy and economic assessment. Journal of Cleaner Production, 2021, 310 : 127493

[17] GoswamiR, SahaS, DasguptaP. Sustainability assessment of smallholder farms in developing countries. Agroecology and Sustainable Food Systems, 2017, 41( 5): 546–569

[18] OenemaO. Toward agriculture green development. Frontiers of Agricultural Science and Engineering, 2020, 7( 1): 110–111

[19] WangM, WuW, LiuW, BaoY. Life cycle assessment of the winter wheat-summer maize production system on the North China Plain. International Journal of Sustainable Development and World Ecology, 2007, 14( 4): 400–407

[20] LiangL. Environmental impact assessment of circular agriculture based on life cycle assessment: methods and case studies. Dissertation for the Doctoral Degree. Beijing: China Agricultural University, 2009 ( in Chinese)

[21] WangC, LiX, GongT, ZhangH. Life cycle assessment of wheat-maize rotation system emphasizing high crop yield and high resource use efficiency in Quzhou County. Journal of Cleaner Production, 2014, 68 : 56–63

[22] HeX, QiaoY, LiangL, KnudsenM T, MartinF. Environmental life cycle assessment of long-term organic rice production in subtropical China. Journal of Cleaner Production, 2018, 176 : 880–888

[23] DongY, XuL, YangZ, ZhengH, ChenL. Aggravation of reactive nitrogen flow driven by human production and consumption in Guangzhou City, China. Nature Communications, 2020, 11( 1): 1209

[24] LamW Y, SimS, KulakM, vanZelm R, SchipperA M, HuijbregtsM A J. Drivers of variability in greenhouse gas footprints of crop production. Journal of Cleaner Production, 2021, 315 : 128121

[25] ZhangD, ZhangW. Low carbon agriculture and a review of calculation methods for crop production carbon foot print accounting. Resources Science, 2016, 38(7): 1395− 1405 ( in Chinese)

[26] Vander Werf H M G, PetitJ. Evaluation of the environmental impact of agriculture at the farm level: a comparison and analysis of 12 indicator-based methods. Agriculture, Ecosystems & Environment, 2002, 93(1−3): 131− 145

[27] ZhangD, ShenJ, ZhangF, LiY, ZhangW. Carbon footprint of grain production in China. Scientific Reports, 2017, 7( 1): 4126

[28] BartlK, VeronesF, HellwegS. Life cycle assessment based evaluation of regional impacts from agricultural production at the Peruvian coast. Environmental Science & Technology, 2012, 46( 18): 9872–9880

[29] SmithA, SnappS, ChikowoR, ThorneP, BekundaM, GloverJ. Measuring sustainable intensification in smallholder agroecosystems: a review. Global Food Security, 2017, 12 : 127–138

[30] HsuA, JohnsonA, LloydA. Measuring progress: a practical guide from the developers of the Environmental Performance Index (EPI). New Haven: Yale Center for Environmental Law & Policy, 2013

[31] HeX, QiaoY, LiuY, DendlerL, YinC, MartinF. Environmental impact assessment of organic and conventional tomato production in urban greenhouses of Beijing city, China. Journal of Cleaner Production, 2016, 134 : 251–258

[32] StreimikisJ, BalezentisT. Agricultural sustainability assessment framework integrating sustainable development goals and interlinked priorities of environmental, climate and agriculture policies. Sustainable Development, 2020, 28( 6): 1702–1712

[33] ZhangX, DavidsonE A, MauzerallD L, SearchingerT D, DumasP, ShenY. Managing nitrogen for sustainable development. Nature, 2015, 528( 7580): 51–59

[34] QuanZ, ZhangX, FangY, DavidsonE A. Different quantification approaches for nitrogen use efficiency lead to divergent estimates with varying advantages. Nature Food, 2021, 2( 4): 241–245

[35] LiuZ, YuN, CamberatoJ J, GaoJ, LiuP, ZhaoB, ZhangJ. Crop production kept stable and sustainable with the decrease of nitrogen rate in North China Plain: an economic and environmental assessment over 8 years. Scientific Reports, 2019, 9( 1): 19335

[36] LiuZ, ChenZ, MaP, MengY, ZhouJ. Effects of tillage, mulching and N management on yield, water productivity, N uptake and residual soil nitrate in a long-term wheat-summer maize cropping system. Field Crops Research, 2017, 213 : 154–164

[37] XiaoG, ZhaoZ, LiangL, MengF, WuW, GuoY. Improving nitrogen and water use efficiency in a wheat-maize rotation system in the North China Plain using optimized farming practices. Agricultural Water Management, 2019, 212 : 172–180

[38] ZhangL X, UlgiatiS, YangZ F, ChenB. Emergy evaluation and economic analysis of three wetland fish farming systems in Nansi Lake area, China. Journal of Environmental Management, 2011, 92( 3): 683–694

[39] OdumH T. Environmental Accounting—Emergy and Environmental Decision Making. New York: Wiley, 1996

[40] PulselliF M, PatriziN, FocardiS. Calculation of the unit emergy value of water in an Italian watershed. Ecological Modelling, 2011, 222( 16): 2929–2938

[41] WangX, ChenY, SuiP, GaoW, QinF, ZhangJ, WuX. Emergy analysis of grain production systems on large-scale farms in the North China Plain based on LCA. Agricultural Systems, 2014, 128 : 66–78

[42] ZhuangM, LiuY, YangY, ZhangQ, YingH, YinY, CuiZ. The sustainability of staple crops in China can be substantially improved through localized strategies. Renewable & Sustainable Energy Reviews, 2022, 154 : 111893

[43] ZhangX, ShenJ, WangY, QiY, LiaoW, ShuiW, LiL, QiH, YuX. An environmental sustainability assessment of China’s cement industry based on emergy. Ecological Indicators, 2017, 72 : 452–458

[44] IntergovernmentalPanel on Climate Change (IPCC). Climate Change 2013: the Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, 2014

[45] XuD, Zhu Q, RosG, CaiZ, WenS, XuM, Zhang F, deVries W. Calculation of spatially explicit amounts and intervals of agricultural lime applications at county-level in China. Science of the Total Environment, 2022, 806(Pt 4): 150955

[46] ChenX F, ChuaiX M, YangL Y. Status quo,historical evolution and causes of eutrophication in lakes in typical lake regions of China. Journal of Ecology and Rural Environment, 2014, 30(4): 438− 443 ( 438)

[47] LeMoal M, Gascuel-OdouxC, MénesguenA, SouchonY, ÉtrillardC, LevainA, MoatarF, PannardA, SouchuP, LefebvreA, PinayG. Eutrophication: a new wine in an old bottle? Science of the Total Environment, 2019, 651(Pt 1): 1–11

[48] JollietO, RosenbaumR, McKoneT E, ScheringerM, vanStraalen N, WaniaF. Establishing a framework for life cycle toxicity assessment. Findings of the Lausanne review workshop. International Journal of Life Cycle Assessment, 2006, 11( 3): 209–212

[49] GentilC, Basset-MensC, ManteauxS, MottesC, MaillardE, BiardY, FantkeP. Coupling pesticide emission and toxicity characterization models for LCA: application to open-field tomato production in Martinique. Journal of Cleaner Production, 2020, 277 : 124099

[50] XuJ, Zhang Z. Research on land sustainability evaluation indices in China for SDGs. Geography and Geo-Information Science, 2020, 36(4): 77− 84 ( in Chinese)

[51] LiuC, XuY, Sun P, LiuJ. Progress and prospects of multi-functionality of land use research. Progress in Geography, 2016, 35(9): 1087− 1099 ( in Chinese)

[52] WangJ J, JingY Y, ZhangC F, ZhaoJ H. Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renewable & Sustainable Energy Reviews, 2009, 13( 9): 2263–2278

[53] ZhangJ, LiY, LiY, ZhangJ, ZhangF. Advances in the indicator system and evaluation approaches of soil health. Acta Pedologica Sinica, 2022, 59( 3): 603–616

[54] LehmannJ, BossioD A, Kögel-KnabnerI, RilligM C. The concept and future prospects of soil health. Nature Reviews: Earth & Environment, 2020, 1( 10): 544–553

[55] XueR, WangC, LiuM, ZhangD, LiK, Li N. A new method for soil health assessment based on Analytic Hierarchy Process and meta-analysis. Science of the Total Environment, 2019, 650(Pt 2): 2771–2777

[56] ZhengX, YangQ. Progress of agricultural biodiversity conservation in China. Biodiversity Science, 2021, 29(2): 167− 176 ( in Chinese)

[57] SunY, LiX, Zhang H, ChenB, LiY, Liu Y, YuZ. Functions and countermeasures of biodiversity conservation in agricultural landscapes: a review. Chinese Journal of Eco-Agriculture, 2017, 25(7): 993− 1001 ( in Chinese)

[58] AdlerF R. The effects of intraspecific density dependence on species richness and species abundance distributions. Theoretical Ecology, 2011, 4( 2): 153–162

[59] SpellerbergI F, FedorP J. A tribute to Claude Shannon (1916–2001) and a plea for more rigorous use of species richness, species diversity and the ‘Shannon-Wiener’ Index. Global Ecology and Biogeography, 2003, 12( 3): 177–179

[60] NationalDevelopment and Reform Commission. People’s Republic of China (NDRC). Compilation of National Cost-benefit Data of Agricultural Product-2019. Beijing: China Statistics Press, 2019 ( in Chinese)

[61] QiangX, ZhouX, LiC, Guo D, LiuZ, ZhangJ. Effect of liquid film mulching on growth and yield of summer maize under different soil moisture conditions. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(1): 54− 60 ( in Chinese)

[62] LuoZ, HuangG, CaiL, ZhangR, LiL, Xie J. Assessment indicators of soil quality in rain-fed areas of the Loess Plateau. Chinese Journal of Eco-Agriculture, 2012, 20(2): 127− 137 ( in Chinese)

[63] CuiJ. A revised integrated framework to evaluate the sustainability of given cropping systems. Dissertation for the Doctoral Degree. Beijing: China Agricultural University, 2020 ( in Chinese)

[64] GuillaumieL, BoiralO, BaghdadliA, MercilleG. Integrating sustainable nutrition into health-related institutions: a systematic review of the literature. Canadian Journal of Public Health, 2020, 111( 6): 845–861

[65] WillettW, RockströmJ, LokenB, SpringmannM, LangT, VermeulenS, GarnettT, TilmanD, DeClerckF, WoodA, JonellM, ClarkM, GordonL J, FanzoJ, HawkesC, ZuraykR, RiveraJ A, DeVries W, MajeleSibanda L, AfshinA, ChaudharyA, HerreroM, AgustinaR, BrancaF, LarteyA, FanS, CronaB, FoxE, BignetV, TroellM, LindahlT, SinghS, CornellS E, SrinathReddy K, NarainS, NishtarS, MurrayC J L. Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems. Lancet, 2019, 393( 10170): 447–492

[66] FanzoJ, BellowsA L, SpikerM L, Thorne-LymanA L, BloemM W. The importance of food systems and the environment for nutrition. American Journal of Clinical Nutrition, 2021, 113( 1): 7–16

[67] PaddaI U H, HameedA. Estimating multidimensional poverty levels in rural Pakistan: a contribution to sustainable development policies. Journal of Cleaner Production, 2018, 197 : 435–442

[68] LiuY, LiuJ, ZhouY. Spatio-temporal patterns of rural poverty in China and targeted poverty alleviation strategies. Journal of Rural Studies, 2017, 52 : 66–75

[69] SachsJ, Schmidt-TraubG, KrollC, LafortuneG, FullerG. Sustainable Development Report 2019. New York: Bertelsmann Stiftung and Sustainable Development Solutions Network (SDSN), 2019

[70] LiepinsR. Women in agriculture: advocates for a gendered sustainable agriculture. Australian Geographer, 1995, 26( 2): 118–126

[71] GhoshS, SenL C, MaliS K, IslamM M, BakchiJ. The role of rural women in household food security and nutrition management in Bangladesh. Asian Journal of Women’s Studies, 2021, 27( 3): 441–459

[72] AsianDevelopment Bank (ADB). Gender equality and food security: women’s empowerment as a tool against hunger. Manila: ADB, 2013

[73] GanX, FernandezI C, GuoJ, WilsonM, ZhaoY, ZhouB, WuJ. When to use what: Methods for weighting and aggregating sustainability indicators. Ecological Indicators, 2017, 81 : 491–502

[74] MikulicJ, KozicI, KresicD. Weighting indicators of tourism sustainability: a critical note. Ecological Indicators, 2015, 48 : 312–314

[75] ZhaoW, LinJ, WangS F, LiuJ L, ChenZ R, KouW J. Influence of human activities on groundwater environment based on coefficient variation method. Environmental Sciences, 2013, 34(4): 1277–1283 ( in Chinese)

[76] PenaJ, NapolesG, SalgueiroY. Implicit and hybrid methods for attribute weighting in multi-attribute decision-making: a review study. Artificial Intelligence Review, 2021, 54( 5): 3817–3847

[77] LiuQ, WuX. Review on the weighting methods of indexes in the multi-factor evaluation. Knowledge Management Forum, 2017, 2(6): 500− 510 ( in Chinese)

[78] HwangC L, YoonK. Multiple attribute decision making-methods and application: a state-of-the-art survey. Heidelberg: Springer Berlin, 1981

[79] LiangL W, WangZ B, LiJ X. The effect of urbanization on environmental pollution in rapidly developing urban agglomerations. Journal of Cleaner Production, 2019, 237 : 117649

Acknowledgements

This work was financially supported by China Scholarship Council (201913043).

Compliance with ethics guidelines

Xiaoxia Guo, Chong Wang, and Fusuo Zhang declare that they have no conflicts of interest or financial conflicts to disclose. This article does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

The Author(s) 2022. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)