A Meta-Analysis of Biostimulant Yield Effectiveness in Field Trials

Abstract

Today's agriculture faces many concerns in maintaining crop yield while adapting to climate change and transitioning to more sustainable cultivation practices. The application of plant biostimulants (PBs) is one of the methods that step forward to address these challenges. The advantages of PBs have been reported numerous times. Yet, there is a general lack of quantitative assessment of the overall impact of PBs on crop production. Here we report a comprehensive meta-analysis on biostimulants (focus on non-microbial PBs) of over one thousand pairs of open-field data in a total of 180 qualified studies worldwide. Yield gains in open-field cultivation upon biostimulant application were compared across different parameters: biostimulant category, application method, crop species, climate condition, and soil property. The overall results showed that (1) the add-on yield benefit among all biostimulant categories is on average 17.9% and reached the highest potential via soil treatment; (2) biostimulant applied in arid climates and vegetable cultivation had the highest impact on crop yield; and (3) biostimulants were more efficient in low soil organic matter content, non-neutral, saline, nutrient-insufficient, and sandy soils. This systematic review provides general biostimulant application guidelines and gives consultants and growers insights into achieving an optimal benefit from biostimulant application.

Keywords: biostimulant; climate; crop yield; meta-analysis; open-field trial; soil quality; sustainable agriculture.

Figure 1

The locations of the open-field studies included in the meta-analysis as displayed on the Köppen-Geiger climate classification (Peel et al., 2007) on the world map (Esri, 2009). The studies were grouped based on the six crop categories of cultivation (cereals, legumes, vegetables, fruits, root/tuber crops, and other crops).

Figure 2

Percentage yield response to biostimulant application affected by the biostimulant category and the commercial status of biostimulant products. The point size correlates to the estimate's precision, and the error bars represent 95% confidence intervals (CI) of mean estimated effect sizes. The number of comparisons and studies is indicated in each line. The combined effect estimates and the heterogeneity test on the random-effect model (RE) are summarized at the bottom, where the heterogeneity test is significant (p < 0.001) and I² ≥ 75% implies substantial heterogeneity. Chi, Chitosan; HFA, humic and fulvic acids; PHs, protein hydrolysates; Si, silicons; Phi, phosphite; SWE, seaweed extracts; PE, plant extracts; MLE, moringa leaf extract.

Figure 3

Percentage yield response to biostimulant application affected by the different application management practices, including (A) application method, (B) frequency, (C) concentration, and (D) interannual studies. The point size correlates to the estimate's precision, and the error bars represent 95% confidence intervals (CI) of mean estimated effect sizes. The number of comparisons and studies is indicated in each line or legend. The combined effect estimates and the test of heterogeneity on the models [random-effect models (RE) in (A,B,D), and mixed-effect model (ME) in (C)] were summarized at the bottom, where the heterogeneity test is significant (p < 0.001) and I² ≥ 75% implies substantial heterogeneity.

Figure 4

Percentage yield response to biostimulant application affected by the crop categories. The point size correlates to the estimate's precision, and the error bars represent 95% confidence intervals (CI) of mean estimated effect sizes. The number of comparisons and studies is indicated in each line. The combined effect estimates and the heterogeneity test on the random-effect model (RE) were summarized at the bottom, where the heterogeneity test is significant (p < 0.001) and I² ≥ 75% implies substantial heterogeneity.

Figure 5

Percentage yield response to biostimulant application affected by the climate categories that were subgrouped into main climates and precipitation types. The point size correlates to the estimate's precision, and the error bars represent 95% confidence intervals (CI) of mean estimated effect sizes. The number of comparisons and studies is indicated in each line. The combined effect estimates and the heterogeneity test on the random-effect model (RE) were summarized at the bottom, where the heterogeneity test is significant (p < 0.001) and I² ≥ 75% implies substantial heterogeneity.

Figure 6

Percentage yield response to biostimulant application affected by the soil properties, including soil (A) texture, (B) pH, (C) salinity, and (D) organic matter (%). The point size correlates to the estimate's precision, and the error bars represent 95% confidence intervals (CI) of mean estimated effect sizes. The number of comparisons and studies is indicated in each line or each legend. The combined effect estimates and the heterogeneity test on the models [random-effect models (RE) in (A–C), and mixed-effect model (ME) in (D)] were summarized at the bottom, where the heterogeneity test is significant (p < 0.001) and I² ≥ 75% implies substantial heterogeneity.

Figure 7

Percentage yield response to biostimulant application as affected by the macronutrient levels, including (A) soil total N (%), (B) soil available N (ppm), soil (C) P, and (D) K levels. The point size correlates to the estimate's precision, and the error bars represent 95% confidence intervals (CI) of mean estimated effect sizes. The number of comparisons and studies is indicated in each line or each legend. The combined effect estimates and the heterogeneity test on the models [mixed-effect models (ME) in (A,B), and random-effect models (RE) in (C,D)] were summarized at the bottom, where the heterogeneity test is significant (p < 0.001) and I² ≥ 75% implies substantial heterogeneity.