The present invention relates to methods for controlling or preventing infestation of a plant by a phytopathogenic microorganism of the genus Macrophomina spp., comprising applying to a crop of plants, the locus thereof, or propagation material thereof, a compound according to formula (I), wherein R1, R2, R3, R4, R5, Y, A, B are as defined herein.

##STR00001##

The present invention clones a gene ZmNLP5 from maize, which plays an important regulatory role in nitrogen assimilation, and the open reading frame of which has a DNA sequence shown as SEQ ID NO:1. The transcription factor protein encoded by the ZmNLP5 gene has an amino acid sequence shown as SEQ ID NO:2. The uses of the maize NLP transcription factor ZmNLP5 mentioned above in promoting expression of a nitrogen metabolic key enzyme gene ZmNIR1.1, in promoting expression of a nitrogen metabolic key enzyme gene ZmNIR1.2, in promoting expression of a nitrogen metabolic key enzyme gene ZmNR1.1, in promoting expression of a nitrogen metabolic key enzyme gene ZmNR1.2, in improving nitrogen assimilation in maize, and in promoting elongation growth of maize root in deficient nitrogen environment are further provided.

The present invention is a plant growth promoter containing a lignocellulosic biomass, wherein the lignocellulosic biomass has a lignin content of 40% by mass or more and 60% by mass or less and a contact angle with water of 50° or less.

Example machine learning (ML) methods and systems for characterizing corn growth efficiency (CGE), and generating field management recommendations based on CGE values are disclosed. An example computing system includes one or more processors, and storage media. The media storing an ML model trained using a training agronomic data set labeled with one or more known CGE values corresponding to one or more trial agricultural fields. The media further storing instructions that, when executed, cause the system to: obtain a production agronomic data set corresponding to a target agricultural field; determine one or more input feature vectors based on the production agronomic data set; process the one or more input feature vectors, with the ML model, to generate one or more predicted CGE values for one or more portions of the target agricultural field; and provide the one or more predicted CGE values as an output.

Example machine learning (ML) methods and systems for characterizing corn growth efficiency (CGE), and generating field management recommendations based on CGE values are disclosed. An example computing system includes one or more processors, and storage media. The media storing an ML model trained using a training agronomic data set labeled with one or more known CGE values corresponding to one or more trial agricultural fields. The media further storing instructions that, when executed, cause the system to: obtain a production agronomic data set corresponding to a target agricultural field; determine one or more input feature vectors based on the production agronomic data set; process the one or more input feature vectors, with the ML model, to generate one or more predicted CGE values for one or more portions of the target agricultural field; and provide the one or more predicted CGE values as an output.

Described herein is the evaluation of the antifungal properties of bacterial strain KGS-3 against Fusarium Head Blight (FHB)) white mold, blackleg and a number of potato fungal diseases and the plant growth effect attained. KGS-3 is a novel strain of Paenibacillus polymyxa that can suppress bacterial and fungal plant diseases. KGS-3 is predicted to produce antifungal metabolites polymyxin, fusaricidin, and paenilarvin and has been demonstrated to produce cylindrol B. KGS-3 is a plant growth promoting bacteria demonstrated to increase protein content of plants and/or plant products.

Provided is a soil conditioner that comprises, as an active ingredient, a compound represented by general formula (1), a salt thereof or a solvate of the same. In general formula (1), R.sup.1 to R.sup.6 independently represent an optionally substituted hydrocarbon group having 1-5 carbon atoms, an optionally substituted alkoxy group having 1-5 carbon atoms, an amino group, a hydroxy group, a halogen atom or a hydrogen atom.

##STR00001##

Provided is a soil conditioner that comprises, as an active ingredient, a compound represented by general formula (1), a salt thereof or a solvate of the same. In general formula (1), R.sup.1 to R.sup.6 independently represent an optionally substituted hydrocarbon group having 1-5 carbon atoms, an optionally substituted alkoxy group having 1-5 carbon atoms, an amino group, a hydroxy group, a halogen atom or a hydrogen atom.

##STR00001##

A method for plant treatment, including: receiving a first measurement for a plant from a sensor as the sensor moves within a geographic area comprising a plurality of plants; in response to receipt of the first measurement and prior to receipt of a second measurement for a second plant of the plurality, determining a set of treatment mechanism operation parameters for the plant to optimize a geographic area output parameter based on the first measurement and historical measurements for the geographic area; determining an initial treatment parameter for the plant; and operating a treatment mechanism in a treatment mode based on the set of operating parameters in response to satisfaction of the initial treatment parameter.

A method for plant treatment, including: receiving a first measurement for a plant from a sensor as the sensor moves within a geographic area comprising a plurality of plants; in response to receipt of the first measurement and prior to receipt of a second measurement for a second plant of the plurality, determining a set of treatment mechanism operation parameters for the plant to optimize a geographic area output parameter based on the first measurement and historical measurements for the geographic area; determining an initial treatment parameter for the plant; and operating a treatment mechanism in a treatment mode based on the set of operating parameters in response to satisfaction of the initial treatment parameter.