03431naa a2200433 a 450000100080000000500110000800800410001902200140006002400510007410000150012524501170014026000090025752022000026665000210246665000150248765000220250265000160252465000280254065000130256865000210258165000210260265000390262365000200266265000180268265000100270065000210271065000140273165000090274565000090275465300190276365300180278265300220280065300310282270000180285370000210287170000300289270000210292277300540294321565102023-09-08       2023    bl uuuu     u00u1 u    #d  a1664-462X7 ahttps://doi.org/10.3389/fpls.2023.12141122DOI1 aMENDES, R.  aA multi-attribute approach to evaluating the impact of biostimulants on crop performance.h[electronic resource]  c2023  aAn ever-growing collection of commercial biostimulants is becoming available in a wide variety of forms and compositions to improve crop performance. Given the intricate nature of deciphering the underlying mechanisms of commercial products, which typically comprise various biological components, it is crucial for research in this area to have robust tools to demonstrate their effectiveness in field trials. Here, we took a multi-attribute approach to evaluating the impact of biostimulants on crop performance. First, we assessed the impact of a biostimulant on the soil and rhizosphere microbiomes associated to crops in eight reference farms, including corn (3 farms), soybean (2), cotton (2) and sugarcane (1), in different biomes and production contexts in Brazil and Paraguay. Second, we modeled a set of integrated indicators to measure crop responses to biostimulant application, including five analytical themes as follows: i) crop development and production (9 indicators), ii) soil chemistry (9), iii) soil physics (5), iv) soil biology (6) and v) plant health (10). Amplicon 16S rRNA and ITS sequencing revealed that the use of the biostimulant consistently changes the structure of bacterial and fungal communities associated with the production system for all evaluated crops. In the rhizosphere samples, the most responsive bacterial taxa to biostimulant application were Prevotella in cotton; Prauserella and Methylovirgula in corn; and Methylocapsa in sugar cane. The most responsive fungal taxa to biostimulant use were Arachnomyces in soybean and cotton; and Rhizophlyctis in corn. The proposed integrated indicators yielded highly favorable positive impact indices (averaging at 0.80), indicating that biostimulant-treated fields correlate with better plant development and crop performance. Prominent indices were observed for indicators in four themes: soil biology (average index 0.84), crop production (0.81), soil physics (compaction reduction 0.81), and chemical fertility (0.75). The multi-attribute approach employed in this study offers an effective strategy for assessing the efficacy of biostimulant products across a wide range of crops and production systems.  aGrowth promotion  aMicrobiome  aPlant development  aRhizosphere  aSustainable agriculture  aAlgodão  aBiologia do Solo  aCana de Açúcar  aEstimulante de Crescimento Vegetal  aFísica do Solo  aMicrorganismo  aMilho  aQuímica do Solo  aRizosfera  aSoja  aSolo  aBioestimulante  aFitossanidade  aImpact assessment  aMulti-attribute indicators1 aBARROS, I. de1 aD'ANDRÉA, P. A.1 aD'ANDRÉA-KÜHL, M. S. C.1 aRODRIGUES, G. S.  tFrontiers in Plant Sciencegv. 14, 1214112, 2023.