The effects of different biochar-dairy manure co-composts on soil moisture and nutrients retention, greenhouse gas emissions, and tomato productivity: Observations from a soil column experiment

Abstract

Finding feasible solutions for sustainable food production is challenging. Here we try to understand the balance between crop productivity and ecological stewardship using agroecological-based soil management strategies. We evaluated the potential of different organic materials such as dairy manure compost and different biochar manure co-composts, derived locally from agricultural wastes, to enhance soil ecosystem services. We assessed their potential impact on soil moisture and nutrient retention, greenhouse gas emissions, and crop productivity using data collected from an outdoor tomato column study. Results from the experiment showed potential of biochar co-composts to positively affect soil health by lessening loss of essential nutrients such as NO₃⁻-N and NH₄⁺-N, sustained tomato yield, and uphold crop water use efficiency. However, yield response to soil organic amendment is constrained by external factors such as irrigation strategies, with treatments under deficit irrigation greatly impacted. Overall, we observed a positive effect of adding biochar manure co-composts to soil, although best management practices are needed to optimize crop productivity and avoid unintentional consequences.

Abbreviations
• ACB
• almond clipping biochar manure co-compost
• ASB
• almond shell biochar manure co-compost
• EC
• electrical conductivity
• ET_c
• crop evapotranspiration
• GHG
• greenhouse gas
• K_c
• crop coefficient
• NUE
• nitrogen use efficiency
• PVC
• polyvinyl chloride
• WSB
• walnut shell biochar manure co-compost
• WUE
• water use efficiency

INTRODUCTION

Maintaining a balance between food production and ecological stewardship is crucial for the sustainability of agriculture and human welfare. Still, farming systems like crop and animal production are known to be associated with several environmental issues, directly linked to soil and nutrient management such as non-point source contamination caused by leaching and field runoff. Common farming operations (e.g., irrigation scheduling and soil compaction caused by farm machinery) can also lead to unintended consequences and loss in farm revenue. Equally, the annual surplus of wastes generated from agriculture production also conveys a high degree of ecological risks, particularly linked to climate change with modern practices like open burning and stockpiling of animal waste (e.g., dairy manure). These practices emit substantial greenhouse gas (GHG), such as methane (CH₄), carbon dioxide (CO₂), and nitrous oxide (N₂O) back into the atmosphere (Crippa et al., 2021).

Alternatively, diverting and converting these wastes into material like biochar and compost to be used as a nutrient-rich organic soil amendment for crop production has been viewed as an ecological technique to enhance soil ecosystem services (Wang et al., 2019; Xiao et al., 2017; Yin et al., 2021). Many studies have investigated and observed a positive effect of incorporating biochar and compost on soil health (Abideen et al., 2020; Chen et al., 2020; Cooper et al., 2020; Shin et al., 2018) and plant growth (Bashir et al., 2020; Obadi et al., 2020; Teodoro et al., 2020). However, the process of creating organic materials such as compost also releases considerable GHGs (Nordahl et al., 2023; Sánchez et al., 2015; Yin et al., 2021). By contrast, composting with biochar has been shown to significantly reduce GHG emission during the composting process (Harrison et al., 2022; Xiao et al., 2017) and may yield a superior organic soil amendment (Sanchez et al., 2018; Wang et al., 2019). An extensive literature review conducted by Agegnehu et al. (2017) highlights the greater potential of biochar co-compost to positively impact soil ecosystem services, as compared to biochar or compost alone. Similarly, a more recent meta-analysis conducted by Wang et al. (2019) also reveals a substantial gain in yield (39.7%) for cereal crops grown with biochar co-compost, though much remains unknown about how agroecosystems respond to biochar co-compost application (Gao et al., 2023). Furthermore, balancing ecological stewardship and high levels of crop production remains a difficult task to resolve (Tully & Ryals, 2017). Few controlled studies have examined the agronomic and environmental effects of soil organic amendment like biochar manure co-compost on soil health, GHGs emission, and crop productivity.

Research objectives

The overall research objective in this study is to understand the balance between agroecological-based soil management and crop productivity. We assessed different soil organic amendments (e.g., manure compost and biochar manure co-compost) that are derived locally from agricultural waste materials (e.g., dairy manure and different orchard wastes). We hypothesized that the application of all organic amendments will have positive effects on soil health, more specifically soil moisture and nutrient retention, and enhance crop productivity, while also reducing environmental impacts on typical agricultural soils in the Central Valley of California. To test this hypothesis, we conducted and discussed here findings from an outdoor tomato column study.

Core Ideas

• Effects of different biochar manure co-composts on soil ecosystem services and tomato productivity were evaluated.
• A 75% deficit irrigation rate was also imposed on soil treatments.
• Organic amendment exhibits higher nitrogen retention potential and sustained crop yield with minimal effect on water use efficiency.
• Plant physiological response to water stress overshadows response to soil organic amendments.
• Biochar-dairy manure co-composts are a superior soil amendment compared to manure compost alone.

MATERIALS AND METHODS Tomato column experiment

The soil column experiment was conducted outside at University of California, Merced, next to the greenhouse facility (37.364 °N, −120.422 °W) during the summer (July) of 2022 and lasted until early winter (November) 2022. The experiment was conducted outside rather than in the greenhouse to expose the plant to actual, fluctuating climatic conditions. The soil used in the column study was collected from an experimental field site and classified as a loamy, thermic Natric Durixeralf USDA Soil Taxonomic family (soil order: Alfisols) (Gao et al., 2023). The experimental design was a complete randomized block design with three blocks (Figure S1). The main factor was the soil organic amendments, which consisted of five different treatments: a no amendment (Control), dairy manure compost (Compost), almond shell biochar manure co-compost (ASB), walnut shell biochar manure co-compost (WSB), and almond clipping biochar manure co-compost (ACB). Each soil organic amendment was applied at a 5% (w/w) application rate and was thoroughly mixed, then backfilled into the top 15 cm. The biochars used for the co-composting process were products derived from local agricultural wastes using a rotational mobile pyrolyzer at temperatures around 350–400°C (Figure S2). Each biochar was then added to fresh dairy manure using a 5% mixture rate (w/w) and composted for 42 days using lab compost reactors. The sub-factor was irrigation schemes, where half of the treatments within each block were irrigated at full crop water demand or crop evapotranspiration (ET_c) while the other half were irrigated at 75% ET_c, a practice known as deficit irrigation. The deficit irrigation served as a means to evaluate the influence of these soil organic amendments on plant productivity given that water is a constraining variable in drought prone agricultural regions such as California. A total of 30 columns (5 treatments × 2 irrigation × 3 replication) were established. The soil column was made from a polyvinyl chloride (PVC) tube with a 15 cm diameter top, a circumference of 53 cm, and a height of 70 cm. The bottom of each column was capped, and a drainage line attached to a 12 oz plastic bottle was installed at the bottom to capture leachate.

Column measurement

Organic beefsteak tomato (Solanum lycopersicum) was chosen as the crop for the column study. The rationale for selecting this crop is because it followed the wheat–tomato crop rotation at the field site (Gao et al., 2023). In addition, tomato holds a major role in the California vegetable industry, with the majority of acreage harvested in the Central Valley (USDA-NASS, 2020). This tomato variety is generally planted from March through July in the region. The column was spaced 30.5 cm apart from each other on a wooden stand, similar to standard field spacing. Tomato was transplanted on July 13, 2022, and harvested by November 23, 2022, a total of 133 growing days. Measurements used as proxy for plant productivity as influenced by soil treatments and irrigation regimes were fractional green canopy and leaf chlorophyll content. Plant canopies (n = 3) were estimated using the Canopeo apps developed by Patrignani and Ochsner (2015) and images were taken at a height of 50 cm above the plant canopy near solar noon. Leaf chlorophyll content was measured using the SPAD 502 meter (Spectrum Technologies, Plainfield, IL, USA) on three youngest mature leaves in each plot (n = 9). The SPAD 502 is a portable and nondestructive technique used to estimate leaf chlorophyll concentrations (Xiong et al., 2015) and has been shown to greatly enhance nitrogen fertilization management (Ghosh et al., 2020). In addition, we also monitored fruit set in each plot throughout the season and used the observation as an indicator of plant stress.

Daily tomato ET_c was calculated by multiplying the reference ET generated by the nearby CIMIS station (#148) by a specific crop coefficient (k_c) for tomatoes grown in the Central Valley region (Hanson & May, 2006). Excluding the water used to bring the initial soil condition to field capacity and plant establishment, a total of 558 and 432 mm of water was applied to the 100% and 75% ET_c plot, respectively. Furthermore, a total of 168 kg N ha⁻¹ was applied to all plots; separated into three equal applications (56 kg N ha⁻¹, applied in August, September, and October 2022). At harvest, tomato fruits were picked from each plot and weighted. Fruit juice was then extracted from all fruit and used for sugar content determination. Sugar concentration (%) was measured using a handheld Brix Refractometer with the capacity to measure sugar density in solution from 0% to 90%. This specific unit also includes automatic temperature compensation (between 10 and 30°C) with an accuracy of ±0.2%. A total of three sugar measurements were conducted for each plot (n = 3). Plant biomass was determined by oven drying the aboveground biomass at 100°C for 48 h. Water use efficiency (WUE) and nitrogen use efficiency (NUE) were calculated by dividing the respective yield in each plot with total water and nitrogen applied.

Soil moisture retention

Since soil moisture retention as influenced by different organic amendments is a main interest, moisture retention curves (n = 3) were established for all soil organic treatments and control using the WP4C Dewpoint Potentiometer (METER Group, Pullman, WA). Here, we assumed that any potential enhancement in moisture retention from the pure material will also be reflected in the amended soil (at a ratio equivalent to 5% (w/w) application rate). The method used to develop moisture retention curve followed the vapor sorption analysis described in Tuller and Or (2005). The moisture retention curve of a substance describes its potential to hold onto water in the presence of different suctions. For field application, understanding the retention curve is extremely critical as moisture is directly linked to many factors such as plant available water, nutrient leaching, biota activity, and GHGs emission. Measurement of hydrophobicity, or the repulsive nature of a material to water, was also performed for all experimental treatments (n = 9) using the water droplet penetration test (WDPT).

Similarly, soil moisture sensors were also essential tools in the column experiment. Ten soil moisture sensors connected to two EM50 data loggers (METER Group, Pullman, WA) were installed in the deficit irrigation plots in Block 2 (randomly selected). Of this, five Decagon 5EM sensors with the capacity to measure soil moisture and temperature were installed at 20 cm depth, or right below the zone of application, to monitor moisture retention near the active rootzone (referred to as surface moisture from hereafter). Five Decagon 5TE sensors, with the capacity to measure soil moisture, temperature, and bulk electrical conductivity (EC) were also installed in the same plot at 50 cm soil depth to monitor moisture movement within the profile (referred to as subsurface moisture from hereafter). Moisture sensors were installed 1 month before transplant to allow for environmental acclimation. The two data loggers were set to take readings every hour and soil moisture data were collected on a weekly basis.

Greenhouse gas emissions

GHG (CO₂, CH₄, and N₂O) sampling was conducted using a cavity ring-down laser spectrometer Picarro Gas Analyzer (Picarro G2508, Picarro Inc., Santa Clara, CA) with a 5 min continuous flux reading. The unit is connected to a closed system static chamber (26 cm in diameter and 13 cm tall). Gas fluxes (nmol m⁻² s⁻¹) were calculated in the Picarro Soil Flux Processor program using the exponential model developed by Hutchinson and Mosier (1981) to account for nonlinear changes in headspace concentration (Gao et al., 2023; Harrison et al., 2022). GHG emissions were measured five different times throughout the season: at preplant or after soil treatment incorporation, after the first, second, and third fertilizer applications, and post-harvest. A PVC chamber (26 cm in diameter fitted down to 15 and 30 cm in height) was attached to the tomato column for gas sampling. As the plant canopy developed and expanded, the canopy was carefully packed inside the chamber to avoid physical damage. After each sampling, gas concentrations were given time to return to ambient concentration before the next measurement.

Nutrient retention and soil analysis

We evaluated the soil organic treatments' potential to retain essential nutrients, such as nitrate nitrogen (NO₃⁻-N) and ammonium nitrogen (NH₄⁺-N), by collecting and analyzing leachate and soil samples. We are particularly interested in these two forms of inorganic nitrogen since they are the primary nitrogen sources for plant uptake but are also susceptible to leaching and are a major environmental and public health concern in the state (Harter et al., 2002). However, in our study no leachate was detected during the growing season, hence after the post-harvest GHG sampling, we conducted a flush on the soil column. Each column was given a total of 102 mm (∼4 in.) water to flush out any residual nutrients within the soil profile. Leachate samples were then collected from all plots twice, the day after the flushing event and 5 days afterward, following a large precipitation event. Leachate samples collected in both respective dates were thoroughly mixed, and a subsample from each was taken for inorganic nitrogen analysis. After the flushing event, soil samples were taken from each column at five depths intervals (0–15, 20–30, 30–40, 40–50, and 50–60 cm) by cutting the entire column into different sections. Moisture content for each sample was determined by oven drying a subsample at 100°C for 48 h. For soil analysis, 6 g of soil were mixed with 30 mL of 2 M KCl and shaken for 1 h. Samples were then filtered using Whatman #1 filter papers into a 50 mL centrifuge tube and frozen at −4°C degree prior to inorganic nitrogen analysis. NO₃⁻-N and NH₄⁺-N concentration (mg L⁻¹) and amount (mg kg dry soil) was determined for leachate and soil samples by the microplate colorimetric techniques using the vanadium-chloride method and salicylate-nitroprusside method, respectively (Mulvaney, 1996).

Statistical analysis

Statistical analyses were done using the R package (R Core Team, 2020). All datasets were subjected to the normality (Shapiro–Wilk) and homogeneity of variance (Levene's) tests. When necessary, datasets that failed to meet the assumptions of ANOVA were log-transformed prior to analyses. A two-way analysis of variance (ANOVA) was performed to determine statistical differences among treatments for measured parameters for the tomato column experiment, with statistical significance established at the 90% confidence interval or p < 0.1. The rationale for using this lower statistical baseline was because our tomato was transplanted very late in the season and during the high summer temperature (25.8°C is average temperature for July in Merced in 2022 and hottest monthly average). Due to this, all plants were exposed to abnormally higher air temperatures and were irrigated more frequently than usual (same amount for all treatments). The deficit irrigations were imposed later in the season when the tomatoes were already well established. In addition, since the plant was grown inside a constricted environment and next to the greenhouse facility, we also expect more variability within treatments caused by various conditions, for example, column orientation to the sun, shade provided by the facility, heating of the soil column, and so on. Tukey's honestly significant difference tests were used to distinguish significance among treatments. In addition, linear regression was also performed on tomato yield datasets with leaf chlorophyll content and canopy coverage.

RESULTS Crop productivity Crop yield

No statistical difference was detected for aboveground biomass of tomato plants as influenced by soil organic amendments (p = 0.465) or irrigation regime (p = 0.396). For tomato fruit, although we observed greater yield in the amended treatments compared to the control, the difference was also not significant (p = 0.653). Significant treatment difference however was detected for irrigation regime (p = 0.065) with fruit yield in the 100% ET_c greater than the 75% ET_c (Figure 1a). As for sugar content, significant differences were observed for treatment (p = 0.013), irrigation (p = 0.074), and interaction between treatments and irrigation levels (p = 0.001). Since interaction was observed, the sugar dataset was separated by soil organic treatment and reanalyzed as a one-way ANOVA. For treatments irrigated at deficit rate, sugar concentration in WSB (5.89%), Compost (5.89%), and Control (5.50%) were significantly lower than ASB (8.33%) (Table S1). ACB had intermediate sugar concentration (7.11%) and was not significantly different from the other treatments. For treatments irrigated at 100% ET_c, WSB (6.50%) and Compost (7.11%) again has significantly lower sugar content compared to ASB (8.22%), ACB (10%), and Control (9.56%) (Table S1). When comparing fruit sugar content with irrigation regimes, we see much higher concentration in the 75% ET_c treatments, compared to its 100% ET_c counterpart (Figure 1b).

FIGURE 1. Tomato fruit yield (a) and sugar content (b). Mean of three replications with standard error bar. Statistical comparison for irrigation regime by same soil treatment. ACB, almond clipping biochar manure co-compost; ASB, almond shell biochar manure co-compost; Compost, daily manure compost; Control, no amendment; ETc, crop evapotranspiration; WSB, walnut shell biochar manure co-compost.

Water use efficiency and nitrogen use efficiency

We did not see any statistical differences in WUE, expressed as ton per hectare of tomato yield per meter of water applied, for soil organic treatments (p = 0.722). A significant difference was detected for irrigation regimes (p = 0.078), again with WUE in 100% ET_c greater than 75% ET_c (Figure 2). NUE, expressed as ton per hectare of tomato yield per kg of N applied, in the column study followed WUE, with no difference detected for soil treatments (p = 0.653) but was present for irrigation regimes (p = 0.065), with greater NUE observed in 100% ET_c plots (Figure S3).

FIGURE 2. Tomato water use efficiency (WUE). Unit is expressed as ton per hectare per m. Mean of three replications with standard error bar. Statistical comparison for irrigation regime by same soil treatment. ACB, almond clipping biochar manure co-compost; ASB, almond shell biochar manure co-compost; Compost, daily manure compost; Control, no amendment; ETc, crop evapotranspiration; WSB, walnut shell biochar manure co-compost.

Soil moisture retention Moisture retention curves and hydrophobicity

Moisture retention curves for the treatments used in the tomato column study (n = 3) are shown in Figure 3. Here we clearly see that moisture retention in the Control is substantially lower than moisture retention from the organic amendments (Figure 3). In other words, at the same gravimetric water content, water in the organic treatments is held at a much greater force than the Control. When assessing the hydrophobicity of each material, other than the Control all other soil organic amendments were found to be extremely hydrophobic (Table S2). Note that hydrophobicity was observed only on pure materials and not on the mixed soil (data not shown).

FIGURE 3. Moisture retention curves for soil treatments in the tomato column, where x-axis is gravimetric water content and y-axis is suction potential. Mean of three replications with standard error bar. ACB, almond clipping biochar manure co-compost; ASB, almond shell biochar manure co-compost; Compost, daily manure compost; Control, no amendment; ETc, crop evapotranspiration; WSB, walnut shell biochar manure co-compost.

Surface and subsurface moisture

Moisture at the 20 cm surface level shows that deficit WSB and deficit Control had much higher moisture content throughout the entire growing season, compared to surface moisture observed in the deficit Compost and deficit ASB treatments (Figure S4a). At the 50 cm subsurface, moisture content in the deficit WSB and Compost treatments is much higher than the Control, ACB, and ASB (Figure S4b). This same pattern is also reflected in the bulk EC detected by the subsurface sensors (Figure S4c).

Nutrient retention Soil and leachate NO₃⁻-N and NH₄⁺-N

Post flushed soil NO₃⁻-N and NH₄⁺-N with depth for the column experiment is shown in Figure 4. For treatment in the 100% ET_c, we see relatively similar NO₃⁻-N (<0.5 mg NO₃⁻-N per kg dry soil) throughout the soil profile for WSB, Compost, and ACB (Figure 4a). Control was similar to the organic amendments up until 50 and 60 cm. At these depths we observed a spike in soil NO₃⁻-N for the Control. For 75% ET_c, except for ASB, soil NO₃⁻-N increased with depth for all treatments (Figure 4b). After 30 cm, soil NO₃⁻-N was greater for the ACB treatment compared to the others. Figure 4c reveals soil NH₄⁺-N with depths for the 100% ET_c. Soil NH₄⁺-N was similar for all treatments up until 30 cm. After this depth, Compost and WSB have a decline in soil NH₄⁺-N, whereas ASB, ACB, and Control have a spike in soil NH₄⁺-N. Soil NH₄⁺-N in the deficit irrigation plots was generally lower compared to the fully irrigated plots. We also observed a similar soil NH₄⁺-N pattern for all treatments with depth in the deficit plots (Figure 4d).

FIGURE 4. Post flushed soil NO3−-N and NH4+-N with depth for 100% ETc (a, c) and 75% ETc (b, d). Mean of three replications with standard error bar. ACB, almond shell clipping biochar manure co-compost; ASB, almond shell biochar manure co-compost; Compost, daily manure compost; Control, no amendment; ETc, crop evapotranspiration; WSB, walnut shell biochar manure co-compost.

Leachate NO₃⁻-N and NH₄⁺-N concentration (mg L⁻¹) for the tomato column study is displayed in Figure 5. For NO₃⁻-N, no statistical significance was detected for soil treatments (p = 0.658); though the Control was much higher in concentration (Figure 5a). We did however observe a significant difference with irrigation regimes (p = 0.094), with leachate NO₃⁻-N statistically greater in the 75% ET_c plots compared to concentration in the 100% ET_c. No statistical difference was detected for leachate NH₄⁺-N across soil treatments (p = 0.994) or irrigation level (p = 0.477) (Figure 5b).

FIGURE 5. NO3−-N (a) and NH4+-N (b) concentrations in leachate collected at the end of tomato study. Mean of three replications with standard error bar. Statistical comparison for irrigation regime by same soil treatment. ACB, almond shell clipping biochar manure co-compost; ASB, almond shell biochar manure co-compost; Compost, daily manure compost; Control, no amendment; ETc, crop evapotranspiration; WSB, walnut shell biochar manure co-compost.

GHG emissions

Table 1 shows soil GHG emissions at different periods in the column study. At preplant, a significant difference was detected for CO₂ emission among treatments (p = 0.095), with emission from WSA (16.95 mg CO₂ m⁻² s⁻¹) greater than Control (7.24 mg CO₂ m⁻² s⁻¹). Emissions from ASB, ACB, and Compost were not statistically different from each other or from WSA and Control. No statistical difference was observed for CH₄ and N₂O emission at preplant. Differences in CO₂ emissions measured after the first fertilization event were not significant among soil treatments but were statistically greater in the 75% ET_c plots compared to 100% ET_c (p = 0.027). No statistical difference was observed for CH₄ and N₂O across soil treatments and irrigation regimes. GHG emissions measured after the second fertilization were not significantly different across soil treatments and irrigation level. Similarly, gas measurement taken after the third fertilization was not statistically different across treatments and irrigation for CO₂ and CH₄. After the third fertilization event, a significant difference was detected for N₂O emission as influenced by soil treatments (p = 0.03). As shown in Table 1, deficit ACB has the highest N₂O emission (3621 ng N₂O m⁻² s⁻¹), followed by 100% ET_c ACB (2655 ng N₂O m⁻² s⁻¹), whereas N₂O was lowest in the deficit WSB (−2897 ng N₂O m⁻² s⁻¹), followed by deficit Compost (−1859 ng N₂O m⁻² s⁻¹). In general, there was a positive N₂O flux in the fully irrigated treatments and a negative flux (uptake) in the deficit plots on this particular day. GHGs emissions taken at post-harvest were not significantly different across soil treatments and irrigation level.

TABLE 1 Mean greenhouse gas emissions (n = 3), measured with Picarro Gas Analyzer. Data analyzed using a two-way analysis of variance, followed by Tukey's honestly significant difference test given statistical significance detected. Value with bold letters indicate statistically significant (p < 0.10) among soil treatments, whereas value with the * (asterisk) indicates statistical significance across irrigation level.

Preplant	WSA	ASB	ACB	Compost	Control
CO2 (mg CO2 m−2 s−1)a	16.95 a	13.47 ab	11.71 ab	12.67 ab	7.24 b
CH4 (ng CH4 m−2 s−1)	24.46	19.79	112.6	85.51	15.84
N2O (ng N2O m−2 s−1)	897.9	888.3	922.1	615.5	526.2
First fertilization
CO2-100% ETc (mg CO2 m−2 s−1)	30.41	26.55	24.38	29.69	31.62
CH4-100% ETc (ng CH4 m−2 s−1)	131.9	422.3	395.9	−642.2	228.7
N2O-100% ETc (ng N2O m−2 s−1)	13517	2655	7724	17138	4827
CO2-75% ETc (mg CO2 m−2 s−1)b	32.59	36.69 *	35.48 *	35.97 *	33.79
CH4-75% ETc (ng CH4 m−2 s−1)	167.2	184.8	281.5	281.5	369.5
N2O-75% ETc (ng N2O m−2 s−1)	4586	3621	5069	2896	−241.4
Second fertilization
CO2-100% ETc (mg CO2 m−2 s−1)	14.92	22.86	18.39	11.01	21.72
CH4-100% ETc (ng CH4 m−2 s−1)	0.00	51.91	193.55	52.79	105.57
N2O-100% ETc (ng N2O m−2 s−1)	−1521	2341	−1279	3621	−555.2
CO2-75% ETc (mg CO2 m−2 s−1)	16.03	14.43	17.67	15.11	19.36
CH4-75% ETc (ng CH4 m−2 s−1)	−20.23	193.6	131.9	140.8	−5.890
N2O-75% ETc (ng N2O m−2 s−1)	482.8	796.6	1448	965.5	4104
Third fertilization
CO2-100% ETc (mg CO2 m−2 s−1)	23.03	25.56	23.51	11.97	35.77
CH4-100% ETc (ng CH4 m−2 s−1)	−167.2	67.74	−404.7	87.98	58.94
N2O-100% ETc (ng N2O m−2 s−1)c	−161.7 bcd	313.8 bc	2655 ab	362.1 bc	1859 ab
CO2-75% ETc (mg CO2 m−2 s−1)	31.62	22.30	30.15	30.34	31.89
CH4-75% ETc (ng CH4 m−2 s−1)	343.1	−281.5	87.98	−290.3	219.9
N2O-75% ETc (ng N2O m−2 s−1)c	−2897 d	−941.4 bc	3621 a	−1859 c	−627.6 bc
Post-harvest
CO2-100% ETc (mg CO2 m−2 s−1)	1.230	1.470	0.600	1.690	1.570
CH4-100% ETc (ng CH4 m−2 s−1)	−11.44	7.650	−64.22	52.79	11.44
N2O-100% ETc (ng N2O m−2 s−1)	161.7	1448	79.66	−917.3	482.8
CO2-75% ETc (mg CO2 m−2 s−1)	1.09-	1.450	1.300	1.010	1.330
CH4-75% ETc (ng CH4 m−2 s−1)	19.35	87.98	64.22	11.44	32.55
N2O-75% ETc (ng N2O m−2 s−1)	482.8	−7483	−1159	1279	989.7

Abbreviations: ACB, almond clipping biochar manure co-compost; ASB, almond shell biochar manure co-compost; Compost, daily manure compost; Control, no amendment; ETc, crop evapotranspiration; WSB, walnut shell biochar manure co-compost.

a

Preplant CO₂ treatment, p = 0.095.

b

First fertilization CO₂ 75% ET_c irrigation, p = 0.0271.

c

Third fertilization N₂O ET_c treatment, p = 0.03.

DISCUSSION Influence on crop productivity

The application of organic amendments to soil is a means to restore soil carbon stocks, mitigate GHG emissions, enhance soil ecosystem services, and sustain crop productivity (Lehmann & Kleber, 2015; Longbottom et al., 2022). However, in this study we observed that the response of crop yield to soil organic amendments varied with irrigation strategies. Although we observed greater yield with soil treatments (Figure 1a), this effect was not statistically different. It appears that yield was more constrained by environmental factors such as water availability. As indicated, most of the statistical differences were detected for irrigation schemes (e.g., fruit, sugar content, leachate NO₃⁻-N, WUE, and NUE) with 100% ET_c greater than 75% ET_c.

Crop productivity under irrigation schemes

Plant measurements such as leaf chlorophyll content and fractional green canopy may help us understand why no significant difference was observed in yield across soil organic treatments but were observed for irrigation schemes. Fractional green canopy was not significantly different for soil treatments, except for canopy taken in September where ambient temperature was high (Table S3). During this date, plant canopy in the deficit irrigation plots was statistically smaller than the fully irrigated plots (p = 0.0123). This may be an indicator of plant water stress. After this period, seasonal temperature dropped and plant canopy in the deficit plots was able to recover and was comparable to the 100% ET_c (Table S3). This may be the reason why we did not see statistical differences in plant biomass. On the other hand, SPAD meter reveals that in most cases leaf chlorophyll content in ACB, Compost, and Control were significantly lower compared to WSB and ASB (Table S4). This observation was true across treatments and irrigation regimes (Table S4). Leaf chlorophylls are essential for photosynthesis and can be used as a proxy for photosynthetic potential (Suplito et al., 2020; Xiong et al., 2015). In general, plants will expand their canopy in order to intercept as much photosynthetic available radiation as possible for consumption. Therefore, in theory we should see a difference in plant productivity given that a positive relationship for tomato biomass to mean leaf chlorophyll content was observed (r² = 0.288, p = 0.0022) (Table 2). Yet a superior relationship was also detected for biomass to fractional green canopy (r² = 0.594, p < 0.0001) (Table 2), and since plant canopy was not statistically different across soil treatments or irrigation regimes, the effects leaf chlorophyll imposed onto biomass may have been overshadowed.

TABLE 2 Relationship between yield versus mean chlorophyll content and fractional green canopy.

Variables	Slope	Intercept	R2	p value	Significance	Std. error
Biomass versus SPAD	3.677	−160.9	0.288	0.0022	***	1.094
Biomass versus Fc	2.542	−0.970	0.594	6.2E-07	****	0.397
Fruit versus SPAD	0.363	+21.56	0.002	0.8141	ns	1.530
Fruit versus Fc	0.242	+37.50	0.004	0.7439	ns	0.735
Brix versus SPAD	0.297	−9.110	0.220	0.0088	***	0.105
Brix versus Fc	−0.029	+7.916	0.008	0.6196	ns	0.057

Note: ns indicates p > 0.1.

*

p < 0.1.

**

p < 0.05.

***

p < 0.01.

****

p < 0.001.

Fruit yield was significantly affected by irrigation regimes with yield in the deficit plots much lower compared to the full irrigation. This could be linked to the observation stated above where the plant may have been water stressed. Possibly, there could be negative chronic effects on fruit development even after the plant canopy recovered, for example, canopy development at the cost of fruit yet. Studies have shown reduced yield for tomatoes that were exposed to water stress conditions (Cui et al., 2019; Medyouni et al., 2021; Patanè & Cosentino, 2010). Another assumption is stress induced premature fruit in the 75% ET_c, which is supported by fruit-set count taken throughout the season. Figure 6 shows the fruit-set count at different dates with the y-axis being treatment replications. Hence, a treatment reaching three implies that all replications for that treatment set fruit on that particular date. As observed, treatments receiving the deficit rate set fruit earlier in the season compared to the fully irrigated plots. Furthermore, both deficit ASB and ACB had only two replications that produced fruit, while deficit Compost did not set fruit until later in the season. Since the fruit was developed prematurely, fruit may be smaller in size but higher in sugar content at harvest due to longer maturity. Deficit Compost has the lowest yield among the 75% ET_c treatments, whereas Control has the lowest yield among the 100% ET_c. Similar to deficit ASB and ACB, Control in the 100% ET_c also has only two replications that set fruit. It is possible that the low yield in the 100% ET_c Control was caused by nutrient stress. This is revealed in both soil and leachate samples where Control has much higher soil NO₃⁻-N and NH₄⁺-N residuals with depths and concentration (Figures 4 and 5). Since Control was irrigated at full ET_c, the water may have pushed available nitrogen below the rootzone.

FIGURE 6. Fruit-set count taken throughout the season, x-axis is soil organic treatments and y-axis is replications. ACB, almond shell clipping biochar manure co-compost; ASB, almond shell biochar manure co-compost; Compost, daily manure compost; Control, no amendment; WSB, walnut shell biochar manure co-compost.

Influence on soil moisture retention

In this column study, moisture retention curves developed for all soil treatments show higher moisture retention potential for the different organic amendments compared to Control (Figures 3) and thus, hypothetically this should also be reflected in the amended soil. However, actual soil moisture retention under climatic conditions varied. Moisture sensors installed below the zone of application in deficit treatments exhibit similar moisture patterns for WSB and Control, both were relatively higher than ASB, ACB, and Compost. All organic amendments displayed higher moisture at the 50 cm subsurface region compared to Control (Figure S4b). This indicates that more water is percolating down in the profile for soil with co-composts amendments, which can be a positive effect for groundwater recharge but a concern for nutrient leaching. However, both yield and leachate NO₃⁻-N and NH₄⁺-N in the deficit plots were not statistically different across soil treatments (discussed more in Section 4.4). This observation may be linked to augmented soil structure stemmed by more microbial activity from the added organic materials (Anderson et al., 2011). The increase in microbial activity in conjunction with organic matter decomposition may strengthen soil pore connectivity and allow for better water flow (Baiamonte et al., 2019; Bohara et al., 2019). Several studies have detected improvement in soil aggregate stability from soil amended with biochar and manure co-compost (Chen et al., 2020) and attributed the findings to enhanced microbial growth (Cooper et al., 2020; Sanchez-Monedero et al., 2018).

Influence on soil nutrient retention

Nutrient retention is critical for crop production and is a major factor for understanding the balance between soil management and crop productivity (Tully & Ryals, 2017; Wu & Ma, 2015). Improvement in nutrient retention can lead to increased yield potential, decrease environmental impacts, while raising farm revenue by reducing fertilizer expenses. In our study, we observed higher potential of biochar co-composts to retain more inorganic nitrogen (NO₃⁻-N and NH₄⁺-N) while also maintaining crop yield. Although not statistically different, inorganic nitrogen in soil and concentration in the leachate were generally lower in co-compost treatments as compared to Control. This is visibly shown in plots that were irrigated at full crop water demand (Figure 4a,c). Since fruit yield in the 100% ET_c was not affected, this is evident of greater retention at high irrigation rate. When comparing nitrogen loss across irrigation regimes, we observed higher soil NO₃⁻-N with depth (Figure 4a,b) and leachate NO₃⁻-N concentration (Figure 5a) in the deficit plots as compared to the 100% ET_c. This result is expected as more nitrogen should be retained with less water percolation (Di & Cameron, 2002). The irrigation flush after harvest then leached out the preserved nitrogen in the deficit plots. Results from our experiments are aligned with numerous leaching studies that shown high potential of biochar to adsorb NO₃⁻-N and NH₄⁺-N (Bohara et al., 2019; Knowles et al., 2011; Kuo et al., 2020; Shin et al., 2018; Zheng et al., 2013).

Influence on GHGs

No major differences were detected for CH₄ and N₂O emissions taken at different dates across soil treatments and irrigation schemes. CO₂ however was higher for the WSB co-compost treatments at the preplant period compared to Control. This could be due to rapid mineralization of the organic material after soil incorporation (Baiamonte et al., 2019; Bohara et al., 2019). Following this, the higher CO₂ emission detected in the deficit plots after the second fertilization may be associated with more available oxygen. Oxygen limitation conditions, for example, moisture content greater than field capacity, is known to have a negative effect on soil respiration (Ghezzehei et al., 2019; Manzoni et al., 2012). Note that average CO₂ emission was generally higher in deficit plots compared to fully irrigated plots (data not shown).

Offset between agroecological-based soil management and crop productivity

Figure 7 presents the main findings from our column experiment, divided into three major sections as influenced by soil organic treatments: (1) soil hydrological properties, (2) soil nutrient retention and GHG emissions, and (3) crop productivity. As illustrated, all sections are interconnected so changes in one section, either negatively or positively, yield responses from the other two. For instance the increase in soil NO₃⁻-N and NH₄⁺-N retention from co-composts treatments (Figure 4a,c) may be triggered by higher moisture retention potential from the soil treatments (Figure 3), however crop yield was only sustained and not enhanced. Here soil hydrological properties were not only affected by organic treatments but also by irrigation inputs. It is possible that yield was not affected by soil treatments because available nutrients were leached below the active rootzone with irrigation (Gao et al., 2020). The nutrients that were retained may be bound to the organic materials making it not available for plant uptake (Yao et al., 2012). This could explain why we observed higher soil NO₃⁻-N with depth (Figure 4b) and leachate NO₃⁻-N concentration (Figure 5a) in the deficit plots while yield across soil treatments was not impacted.

FIGURE 7. Summary of tomato column experiment. An increase, decrease, or equal is in response to soil organic amendments. ACB, almond shell clipping biochar manure co-compost; ASB, almond shell biochar manure co-compost; Compost, daily manure compost; Control, no amendment; ETc, crop evapotranspiration; WSB, walnut shell biochar manure co-compost.

The response of plants to soil organic amendments and irrigation strategies further complicates the system. For instance, no difference in tomato biomass was detected across soil treatments and irrigation regimes (Table S1), yet fruit yield was significantly impacted in the deficit rate (Figure 1a). Likewise, tomato canopy was similar in size throughout the season for all plots (Table S3), but leaf chlorophyll content in WSB and ASB were constantly higher than ACB, Compost, and Control (Table S4) across irrigation schemes. In particular, the higher leaf chlorophyll content in WSB co-compost may be an indicator of greater nutrient uptake, as revealed in the yield for 100% ET_c. These observations demonstrate plant natural resiliency to adverse conditions but do convey a yield tradeoff (Dutta et al., 2020; Husen, 2021). Here the positive effects from organic treatments as reflected in higher leaf chlorophyll content and greater soil nutrient retention potential may be overshadowed by plant physiological response to water stress. Across soil organic treatments, manure compost under deficit rate seems to underperform in both yield and WUE compared to co-compost treatments.

Ultimately, by adding organic amendment to agricultural soil our goal is to increase crop yield by improving moisture and nutrient uptake (Lehmann & Kleber, 2015). But nutrient retention from organic amendment is less effective under common irrigation practices (Gao et al., 2020). In a 3 year field study, Gao et al. (2020) observed highest N leaching in biochar-treated plots subjected to high irrigation frequency. Yet we also risk stressing the plant and reducing yield potential if we lower the amount of water applied (Medyouni et al., 2021). This shows the intricate nature when trying to couple crop production with ecological management. Moreover, handling such tasks requires a personnel (e.g., farmer or farm manager) to have extensive knowledge in soil, crops, irrigation, and environmental science. This may be a reason why such practices have not been adopted by farmers (Swinton et al., 2015). Similar to the 4R Nutrient Stewardship (Johnston & Bruulsema, 2014) there may be a need to develop best management practices guidelines for soil organic amendments.

CONCLUSION

Overall, we observed positive effects of adding organic amendment such as biochar manure co-compost to soil. Although not statistically significant, we observed potentially greater nutrient retention, but yield response varied and is constrained by irrigation strategies. Our results also show that using biochar manure co-composts as a soil organic amendment has greater potential to positively affect soil ecosystem services, as compared to using organic amendment such as compost alone. Most critically, the transferability of excess wastes from the agriculture sector into a product with potential to sustain crop productivity, lessen ecological concerns, and alleviate climate change may open a holistic route toward sustainable food production.

AUTHOR CONTRIBUTIONS

Touyee Thao: Conceptualization; data curation; formal analysis; investigation; methodology; resources; validation; visualization; writing—original draft. Brendan P. Harrison: Data curation; investigation; validation; writing—review and editing. Si Gao: Project administration; supervision; writing—review and editing. Rebecca Ryals: Funding acquisition; project administration; supervision; writing—review and editing. Ruth Dahlquist-Willard: Project administration; supervision; writing—review and editing. Gerardo C. Diaz: Funding acquisition; project administration; supervision; writing—review and editing. Teamrat A. Ghezzehei: Conceptualization; funding acquisition; project administration; supervision; validation; writing—review and editing.

ACKNOWLEDGMENTS

This works was supported by the Mobile Biochar Production for Methane Emission Reduction and Soil Amendment project funded by California Strategic Growth Council, Climate Research Program under Grant Agreement #CCR20014. The authors acknowledge the help of Mya Star Tabares (FACTs student mentee) for her assistance in the tomato study. The authors would like to express their gratitude to Dr. Hugh McLaughlin for providing us with the biochars.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.