Using organic fertilizers to increase crop yield, economic growth, and soil quality in a temperate farmland

Study area

We conducted this experiment over two years in Jiang Jiazhuang Village, Pingyi County, Shandong Province, Eastern China (35°26′21″N, 117°50′11″E). Our study was approved by the Plant Eco-physiological Research Group (PERG) in the Institute of Botany, Chinese Academy of Sciences (project number: 201707.8). In this study, the name of the farmer is Jianjin Jiang who has a verbal agreement with the PERG in the Institute of Botany, Chinese Academy of Sciences. Although the farmland belongs to Jianjin Jiang, he has already rented it to the PERG. So, we should only get permission of the PERG. Any other permission from the individual and company is not needed. Shandong Province is an important source of food production in the NCP, and its main cropping system is a winter wheat-summer maize rotation. The study area has a typical temperate and monsoonal climate. In 2018 and 2019, the total annual rainfall was 685.1 mm and 842.2 mm, and the average mean air temperature was 15.2 °C and 15.1 °C, respectively. Figure 1 shows the daily precipitation and mean air temperature during our study from 1 October 2017 to 1 October 2019. The experimental soil was identified as an alfisol according to its soil taxonomy (IUSS Working Group WRB, 2014). The main soil (0–20 cm) characteristics are shown in Table 1.

Experimental design

We set up our study using a randomized block design with four treatments and three replicates based on previous related research (Zhen et al., 2014; Guo et al., 2016). Each plot had an area of 80 m² (10 m ×8 m). The four treatments were: (1) RSMT, rapeseed meal treatment, 1,212 kg ha⁻¹ yr⁻¹ (fresh weight) of RSM; (2) SBMT, soybean meal treatment, 1,125 kg ha⁻¹ yr⁻¹ (fresh weight) of SBM; (3) CMT, cattle manure treatment, 10,215 kg ha⁻¹ yr⁻¹(fresh weight) of CM; and (4) CK, no organic fertilizer treatment. We applied the fertilizers according to the local farmers’ average SBM application rate. We distributed equivalent amounts of N (66.7 kg ha⁻¹ yr⁻¹) across the three organic fertilizer treatments. The three organic fertilizers were solid, and their nutrient and water levels are shown in Table 1. All fertilizers were applied one time before winter wheat seeding. The winter wheat cultivar Shannong 32 was sown in mid-October and harvested in early June the following year. The summer maize cultivar Jinhai 5 was sown in mid-June and harvested in early October. 172.5 kg ha⁻¹ and 37.5 kg ha⁻¹ of wheat and maize seeds were planted, respectively. The distance between rows of wheat was 0.3 m, and the distances between rows and between individual maize were 0.6 m and 0.3 m, respectively. All other field management practices remained unchanged. A rotary tiller ridged the plots one time before winter wheat seeding at a plowing layer depth of 30 cm. Crops were irrigated twice during the winter wheat season. No irrigation was necessary during the summer maize season because of the abundance of rainfall. Crops were physically weeded twice during the winter wheat season and once during the summer maize season.

Yield assessment

Three 1 m² crops were randomly harvested from each plot to determine winter wheat yield on 7 June 2018 and 10 June 2019. Three replicates of 10 consecutive summer maize plants in the same row were harvested on 9 October 2018 and 8 October 2019. The seeds of the harvested winter wheat and summer maize had a water content of < 14%.

Farm profitability

We recorded in detail each of the total inputs and outputs of the four treatments. The treated wheat and maize were sold online for $0.89/kg⁻¹ and $0.59/kg⁻¹, respectively. We used the annual average net income to assess economic benefits.

Soil sampling

We sampled the soils for chemical and biological characteristic analysis on 9 October 2018 and 8 October 2019. We used a 5 cm soil auger to select five random sampling points in the 0–20 cm layer, and then mixed the samples together into one. All soils were immediately transported to the laboratory, where all visible stones, roots, and undegraded fertilizer were removed. We divided the soil samples into two groups. One group was air-dried for chemical analysis, and the other group was passed through a 10-mesh sieve for biological analysis.

Soil chemical analysis

We passed a portion of the air-dried soil through a 10-mesh sieve to analyze its soil pH, available N (AN), available P (AP), and available K (AK). The other group of air-dried soils was passed through a 100-mesh sieve to analyze its SOM, TN, total phosphorus (TP), and total potassium (TK). We used a digital pH meter (PB-10, Sartorius, Germany) and a 1:2.5 soil:water ratio to measure soil pH. AN was determined using NaOH hydrolysis (Bao, 2000). We extracted AP by applying 0.5 M NaHCO₃ (1:10, w/v) for 30 min, and determined colorimetrically using the molybdate method (Watanabe & Olsen, 1965). AK was measured using flame photometry (Schollenberger & Simon, 1945). We measured SOM using the dichromate oxidation method (Nelson & Sommers, 1996). TN was determined using the Kjeldahl method (Bremner & Mulvaney, 1982) and the FOSS Kjeltec 8200 (Foss, Hilleroed, Denmark). We used nitric-perchloric digestion (1:1) to extract TP and TK, and used EPA Method 3050 (USEPA, 1986) for analysis.

Soil aggregate distribution

We gathered our soil samples for aggregate distribution analysis from five subsamples (20 cm ×20 cm ×20 cm) of each plot on 8 October 2019. In the laboratory, we gently broke apart the moist soil samples along their natural break points and removed all visible stones, roots, and undegraded fertilizer. The soil samples were then air-dried for aggregate distribution analysis.

The soil aggregates were separated using the wet-sieve method (Elliott, 1986), and four aggregate-size classes were determined (large macroaggregate, >2 mm; small macroaggregate, 0.25–2 mm; microaggregate, 0.053–0.25 mm; and silt + clay, <0.053 mm). A subsample of 100 g air-dried soil was placed on top of a 2 mm sieve gently slaked with deionized water for 5 min. We then passed the slaked soil sample through three sieves with different mesh sizes (2 mm, 0.25 mm, and 0.053 mm) by automatically moving the sieves up and down for 15 min. All fractions left on each sieve and the bottom pan were thoroughly rinsed and transferred to aluminum boxes. We dried and weighed all aggregates to determine their size class.

Soil biological analysis

We analyzed MBC and MBN levels using a chloroform fumigation-direct extraction procedure. We estimated the levels using the difference between the fumigated and non-fumigated soil samples, and a conversion factor of 0.45 (Vance, Brookes & Jenkinson, 1987; Brookes, 1995). We extracted 25 g of fresh soil from each soil sample using 50 ml of 0.5 mol L⁻¹ K₂SO₄ solution. The other 25 g of fresh soil was fumigated with chloroform at room temperature for 24 h, after being extracted with the K₂SO₄ solution. We immediately stored all extracts at −20 °C to be measured using a total organic carbon (TOC) analyzer (multi N/C 3100, Analytik Jena, Germany). We weighed out 10 g of fresh soil to measure its water content and to calculate its MBC and MBN levels.

Phospholipid fatty acid (PLFA) analysis was performed following the procedure described by Bossio et al. (1998). We extracted lipids from freeze dried soil samples at −80 °C using a single-phase chloroform-methanol-citrate buffer mixture (1:2:0.8, v/v/v). The PLFAs were then purified and identified using an Agilent 6850 gas chromatograph with the Sherlock Microbial Identification System (MIDI) V4.5 with the internal standard set at PLFA 19:0. In this study, the PLFA group was made up of three components (Bossio et al., 1998; Fierer, Schimel & Holden, 2003): (1) bacteria, most commonly 14:0, 15:0, 16:0, 17:0, and 18:0; gram-negative bacteria (G–) cy17:0, cy19:0, 15:1 ω6c, 16:1 ω7c, 16:1 ω9c, 17:1 ω8c, 18:1 ω7c, and 18:1 ω9c; and gram-positive bacteria (G+) i14:0, i15:0, a15:0, i16:0, a16:0, i17:0, and a17:0; (2) fungi, most commonly 18:2 ω6c, 18:3 ω6c, 20:1 ω9c (Joergensen & Wichern, 2008), and arbuscular mycorrhizae fungi (AMF) 16:1 ω5c (Olsson, 1999); and (3) actinomycetes 10Me16:0, 10Me17:0, and 10Me18:0.

Soil quality evaluation

We assessed soil quality using the SQI method. The SQI was developed according to the following three steps: (1) selecting a minimum data set (MDS) for soil indicators, (2) scoring the MDS indicators, and (3) integrating the scored indicators into one SQI value (Qiu et al., 2019; Boafo et al., 2020).

We performed one-way analysis of variance on 18 soil indicators. All indicators showed significant differences (P < 0.05) across the four treatments and were included in the total data set (TDS). The TDS soil indicators were employed in principal component analysis (PCA) and selected the components with eigenvalues >1 as principal components (PCs). For each PC, we considered highly loaded indicators as those with absolute loading values within 10% of the highest factor loading. When a PC contained only one highly loaded indicator, this indicator was selected for the MDS. When a PC contained multiple indicators, we determined each indicator’s relevance using Pearson’s correlation analysis. If they were not correlated (r < 0.70), all highly loaded indicators were selected for the MDS. Otherwise, only the indicator with the highest sum of correlation coefficients (absolute value) was selected for the MDS (Qiu et al., 2019; Boafo et al., 2020).

After MDS selection, we used a linear scoring method to transform each soil indicator into a unitless score ranging from 0.00 to 1.00 (Boafo et al., 2020). Indicators were divided into two types according to their soil parameter concentration thresholds: “more is better” and “less is better”. In this study, considering the contributions of the MDS indicators, we only applied the “more is better” function: (1)${\displaystyle Y=\frac{X}{X_{max}}}$

where Y stands for the indicator linear score, X is the soil indicator value, and X_max is the maximum value for each soil indicator.

We weighed the MDS indicators using the PCA results where the percentage of PC variation was divided by the total percentage of variation. We integrated the transformed indicator scores into the SQI using the following weighted equation: (2)${\displaystyle SQI={\sum }_{i=1}^{\mathrm{n}}{W}_i{S}_i}$

where SQI is the soil quality index, S_i is the indicator score, W_i is the weight of each indicator, and n is the number of soil indicators in the MDS. In this study, higher SQI values reflected better organic fertilizer treatment outcomes.

Statistical analyses

We performed statistical analysis using Microsoft Excel (2016) and SPSS 22.0 (IBM Corp, Armonk, New York, USA). Crop yield and soil property differences across the four treatments over the same year were determined using one-way analysis of variance and Duncan’s test. We used an independent sample t-test to assess the differences between different years, 2018 and 2019, over the same treatment. We set the significance level at P < 0.05. Soil microbial community composition (PLFAs) differences across the four treatments were evaluated using a PCA by correlation matrix (Past Version 3.25, Hammer; University of Oslo, Oslo, Norway). During the soil quality evaluation, SPSS 22.0 used both Pearson’s correlation and PCA to determine the MDS indicators and their weights. Figures were created using OriginPro 9.2 (OriginLab, Massachusetts, USA).

Crop yield and economic benefits

Compared to the CK group, RSMT, SBMT, and CMT showed significantly increased yields over the two-year study (P < 0.05, Fig. 2). In 2018, winter wheat and summer maize crop yields were 3,301.5 and 3,805.4 kg ha⁻¹ in the CK group, 6,368.0 and 7,351.8 kg ha⁻¹ in RSMT, 7,565.1 and 8,299.9 kg ha⁻¹in SBMT, and 7,453.8 and 8,227.5 kg ha⁻¹ in CMT, respectively. In 2019, winter wheat and summer maize in RSMT, SBMT, and CMT had 161%, 299%, and 256% greater yields, respectively, than the CK group (P < 0.05). However, the 2019 winter wheat and summer maize CK and RSMT crop yields were significantly lower than those in 2018 (P < 0.05).

As shown in Table 2, RSMT, SBMT, and CMT had much greater outputs than the CK group. We noted the highest annual average net income from SBMT and the lowest annual production cost from CMT. The inputs (materials, equipment, and labor) totaled $1,695.1, $2,431.0, $2,312.2 and $1,900.0/ha⁻¹ for the CK, RSMT, SBMT, and CMT groups, respectively. The annual average production costs for the CK, RSMT, SBMT, and CMT groups were $0.33, $0.21, $0.15 and $0.13/kg⁻¹, respectively. Excluding fertilizer and fertilization costs, the other inputs were consistent across the four treatments. RSM and SBM were priced higher than CM. However, CM required more manual labor during the fertilization process. The annual average net incomes in RSMT, SBMT, and CMT were $6,367.2, $9,318.7, and $9,047.0/ha⁻¹, and were 2.70, 3.95, and 3.84 times higher than the CK group ($2,357.9/ ha⁻¹), respectively. The annual average net incomes in SBMT and CMT were 1.46 and 1.42 times higher, respectively, than RSMT.

Soil chemical characteristics

In this study, we measured 8 soil chemical characteristics (Table 3). CMT had the highest soil pH, followed by the SBMT, RSMT, and CK groups (P < 0.05). SBMT had the highest amounts of SOM, TN, AN, AP, and AK, followed by the CMT, RSMT, and CK groups (P < 0.05, Table 3). RSMT, SBMT, and CMT in 2019 had SOM and TN levels that were 54% and 71%, 102% and 90%, and 88% and 86% higher, respectively, than the CK group (P < 0.05). SBMT displayed the highest TP and TK levels, followed by RSMT and CMT. Soil chemical levels in 2019 were higher than those in 2018 across the three organic fertilizer treatments.

Soil aggregate fractions

Large and small macroaggregates were the most represented aggregate-size classes. We noted that SBMT had the highest amount of macroaggregates, while the CK group had the highest amounts of microaggregates and silt + clay (P < 0.05). The proportion of macroaggregates in RSMT, SBMT, and CMT were 40%, 48%, and 43% greater, respectively, than in the CK group. The proportion of microaggregates in RSMT, SBMT, and CMT were 23%, 27%, and 29% lower, respectively, and the proportion of silt + clay was 38%, 47%, and 33% lower, respectively, than in the CK group (P < 0.05, Table 3).

Soil biological characteristics

Organic fertilizers significantly increased soil MBC and MBN levels (P < 0.05, Table 3). In 2018 and 2019, CMT had the highest MBC and MBN levels, followed by SBMT and RSMT. We found the lowest MBC and MBN levels in the CK group. At the end of the experiment, MBC levels in RSMT, SBMT, and CMT were 73%, 199%, and 208% (P < 0.05) greater, respectively, than in the CK group. MBN levels in RSMT, SBMT, and CMT were 71%, 184%, and 199% greater, respectively, than in the CK group (P < 0.05). RSMT, SBMT, and CMT had MBC levels in 2019 that were 19%, 90%, and 77% greater, respectively, than MBC levels in 2018, and MBN levels in 2019 that were 21%, 75%, and 74% greater, respectively, than MBN levels in 2018 (P < 0.05, Table 3).

The total amount of PLFAs, bacteria, fungi, actinomycetes, and AMF in 2018 and 2019 were highest in SBMT, followed by the CMT, RSMT, and CK groups (Table 3). At the end of the experiment, RSMT, SBMT, and CMT had 36%, 73%, and 56% more PLFAs; 35%, 72%, and 54% more bacteria; 43%, 83%, and 74% more fungi; 34%, 74%, and 58% more actinomycetes; and 52%, 106%, and 86% more AMF, respectively, than the CK group (P < 0.05, Table 3).

We performed 27 PLFAs to PCA in 2018 and in 2019 (Fig. 3). In 2018, principle component 1 (PC1) and principle component 2 (PC2) accounted for 85.7% and 5.2% of the total variance, respectively. SBMT and CMT demonstrated a clear separation along PC1 and PC2 from the RSMT and CK groups (Fig. 3A). In 2019, the first two PCs accounted for 78.2% and 9.9% of the total variance, respectively. Microbial communities from the CK group and the three organic fertilizer treatments showed significant separation in PC1 (P < 0.05). In PC2, CMT was isolated from the other three treatments (P > 0.05, Fig. 3B).

Soil quality evaluation

The PCA results (Table 4) showed that only the first two PCs had eigenvalues >1.0 and 90.917% of the total variability: 82.253% for PC1 and 8.664% for PC2. In PC1, the highly loaded indicators were SOM, TN, TP, AN, AP, AK, MBC, MBN, bacteria, fungi, AMF, total PLFA, and macroaggregates (Table 4). These 13 indicators showed significant correlations among each other (r > 0.70). Since the SOM had the highest absolute loading sum, it was selected for the MDS (Table 5). The highly loaded indicators in PC2 were TK, bacteria, actinomycetes, and total PLFA. We also selected actinomycetes for the SQI MDS because they had the highest absolute correlation coefficient (Table 5).

After selecting SOM and actinomycetes for the MDS, we applied a “more is better” linear scoring function. The highest SOM and actinomycete score values were found in SBMT, followed by CMT, RSMT, and CK (Fig. 4). We determined the weight of each MDS indicator by calculating its contribution percentage in each PC. The weights of SOM and actinomycetes were 0.905 and 0.095, respectively. After scoring the selected MDS indicators, we calculated the SQI using the following weighted equation: (3)${\displaystyle SQI=0.905\times S_{SOM}+0.095\times S_{Actinomycetes}}$

The average SQI values for the CK, RSMT, SBMT, and CMT groups were 0.47, 0.71, 0.94, and 0.87, respectively. The differences across the four treatments were significant (P < 0.05, Fig. 4).

Crop yield and economic benefits

Previous studies have claimed that using organic fertilizers results in an average of 19.8–25% lower crop yield than chemical fertilizers (Ponti, Rijk & Ittersum, 2012; Seufert, Ramankutty & Foley, 2012; Ponisio et al., 2015). However, yield losses have not been the case across all climate zones, organic fertilizers, or crops (Seufert, Ramankutty & Foley, 2012; Gattinger et al., 2013). When using best management practices and suitable organic fertilizers, an organic system’s yield can meet or even surpass a chemical system (Liu et al., 2016). Our results clearly indicate that wheat and maize in SBMT and CMT produced much higher yields than the average in Shandong Province (6,035.0 kg ha⁻¹ for wheat and 6,469.0 kg ha⁻¹ for maize). Therefore, we suggest that the proper application of SBM and CM in a winter wheat-summer maize rotation system would reduce yield losses.

For many farmers, however, crop yield benefits are second to economic benefits. In our previous studies, we found that organic systems presented much higher economic benefits than conventional systems (Liu et al., 2016; Meng et al., 2016). Using economic benefit analysis, we observed the highest input in RSMT, and the lowest annual production costs in CMT, followed by SBMT. Net incomes were higher for SBMT ($9,318.1/ha⁻¹) and CMT ($9,047.0/ha⁻¹), and they were 1.46 and 1.42 times greater, respectively, than RSMT, as well as 4.82 and 4.68 times greater, respectively, than conventionally farmed local crops (Liu et al., 2016). Our results indicate that SBM and CM fertilizers generate more economic benefits than RSM and chemical fertilizers.

Soil chemical characteristics

We found that soil pH in RSMT, SBMT, and CMT was significantly higher than in the CK group (P < 0.05), indicating that organic fertilizers, specifically CM, can alleviate the soil acidification caused by long-term chemical fertilizer application (Guo et al., 2010; Zhu et al., 2018). At the end of our study, we found much higher concentrations of SOM and the minerals essential for plant growth (N, P, and K) in organic fertilizer treatments than in the CK group, which corresponded with the results of Liu et al. (2016). The soil chemical levels for the three organic fertilizer treatments in 2019 were significantly higher than those in 2018, indicating organic fertilizers’ ability to nurse cultivated land. This effect was more obvious over time.

Soil aggregate fractions

Soil aggregates are important indicators of soil quality and the effects of soil management (Barthès & Roose, 2002). Organic fertilizer treatments showed significantly higher proportions of macroaggregates and lower levels of microaggregates and silt + clay than the CK group, which was consistent with the findings of Liao, Boutton & Jastrow (2006) and Yu et al. (2012). These results may be due to the higher amounts of AMF and other fungi (Li et al., 2020) that predominantly proliferate in larger soil pores and contribute to macroaggregate formation (De Gryze et al., 2005; Wilson et al., 2009; Ding & Han, 2014).

Soil biological characteristics

MBC representing about 1–5% of the soil’s TOC could provide an effective warning of the soil quality (Powlson & Brooks, 1987). Our results clearly demonstrated that organic fertilizers significantly increased MBC and MBN levels, which was consistent with the findings of Kaur, Kapoor & Gupta (2005), who found that MBC levels tended to be lower in unfertilized soil compared to soil treated with organic fertilizers. Increased MBC and MBN levels may be caused by organic fertilizers’ abundant metabolizable carbon and N that promote root growth and increase soil microbial biomass (Ekblad & Nordgren, 2002). More importantly, organic fertilizers directly supply energy to microbes, further increasing soil microbial biomass and activity (Vance & Chapin, 2001; Ekblad & Nordgren, 2002).

Our PCA results indicated that organic fertilizers significantly affected the soil microbial community. SBMT and CMT showed higher amounts of total PLFAs, bacteria, fungi, and actinomycetes than RSMT and the CK group. Bacteria represented about 80–85% of the total PLFAs in this study. The increase of bacteria in SBMT and CMT had a positive effect on soil properties, owing to the fact that many types of bacteria (G+ and G–) can easily adapt to environmental changes (McCann et al., 1994; Zhang et al., 2018). G+ and G–produce spores, have thicker cell walls, and can adjust their cell wall structure, making them more adept at surviving environmental changes (McCann et al., 1994; Zhang et al., 2018). Therefore, SBM and CM significantly affected soil properties by changing the soil’s microbial communities.

AMF are an important group of soil microorganisms. In this study, SBMT and CMT had more AMF than RSMT or the CK group, which corresponded with previous research (Oehl et al., 2004; Manoharan et al., 2017). AMF help protect plants against soil pathogens (Sikes, Cottenie & Klironomos, 2009), increase plant drought tolerance (Auge et al., 2001), and provide plants with nutrients (Van der Heijden et al., 1998; Hill et al., 2010; Wagg et al., 2011), meaning that the lower amounts of AMF found in RSMT and CK groups may lead to reduced agricultural land quality and nutrient utilization in the follow-up crops.

Soil quality

In this study, we selected 18 indicators as potential soil quality indicators. Previous studies have also adopted an MDS when using an SQI (Qiu et al., 2019; Boafo et al., 2020), with the most commonly selected indicators being SOM, TN, and MBC (Abid & Lal, 2008; Liu et al., 2014; Bera et al., 2016). Selecting MDS indicators is contingent on various factors. In this study, we selected SOM and actinomycetes as MDS indicators. The SQI values varied across the four treatments and ranged from 0.47 to 0.94. The highest SQI value occurred in SBMT, followed by CMT and RSMT, indicating that SBM application is the most capable at maintaining soil quality.