# Research on Evaluation Methods of Black Soil Farmland Productivity Based on Field Block Scale
## 1 Introduction  
The world population is expected to reach 9 \(\times\) 10\({}^{9}\) by 2050, and in response to this demographic pressure, the demand for world food production will increase by 60-70 percent [1, 2]. Land is a fundamental resource for food production [3], and its quality and productivity are directly related to food supply and national economic stability. Although it is widely recognized that soil is an abundant resource, the reality is that soil resources are being rapidly degraded due to salinization, erosion, compaction, pollution, structural collapse, acidification, organic matter, biological activities, and urban and industrial development [4]. It is necessary to study soil productivity evaluation methods to maintain soil productivity, and finding more effective assessment methods by studying land productivity is essential to improve agricultural production efficiency and promote sustainable agriculture. Research on land productivity evaluation is conducted to gain a comprehensive understanding of land fertility, potential, and its adaptability under different environmental conditions, to support scientific and rational land management, to better plan agricultural production, to reduce pressure on land resources, and to promote the maintenance of ecological balance. Land productivity evaluation methods in past studies relied on laboratory analyses and statistical methods, mainly CI models [5] as well as PI models [6]. [PERSON] et al. [7] summarized the methods related to land productivity evaluation and achieved some results in assessing soil fertility and potential in China; [PERSON] et al. [8] improved the PI model based on PI model applied to the dry-hot valleys in China; [PERSON] [9] confirmed that the PI model is a good prediction model for maize yield; [PERSON] et al. [10]used an improved soil productivity index model to study the process of soil productivity change in post-dam debris flow sediments during the restoration process; however, these research methods are mainly for estimating land productivity, and their final derived land productivity indices are closely related to the observed areas [11]. Although there are advanced methods for uncertainty treatment of estimation, such as the National Commodity Crop Productivity Index (NCCPI) [12], there are always limitations in the estimation of land productivity assessment methods, which rely on the scores given by the experts' experience or the choice of parameter values in the function when constructing the affiliation function in the process of data processing and modelling. Since traditional farming methods are characterized by excessive allocations, which have significant economic and environmental impacts [13], agricultural management requires accurate estimation of land productivity, determination of various combinations of soil composition and nutrient precision to analyze the soil to reduce the cost of cultivation. Machine learning models can effectively avoid this evaluation uncertainty caused by empirical knowledge and subjective judgement, and by learning from soil data, assessment models can be built more quickly and accurately. [PERSON] et al. [14] used a combination of optical and radar remote sensing data to apply the SVM algorithm to build a Soil organic C (SOC) prediction model; [PERSON] et al. [15] collected historical soil data from southern China and combined multivariate linear model (MLM) and mixed effects regression model (MEM) for soil productivity assessment; [PERSON] et al. [16] obtained 1781 sets of maize farmland data comparison in Northern Nigeria using linear regression models, as well as random forest machine learning to predict maize yields based on nutrient concentrations in spike leaves; [PERSON] et al. [17] provided an estimate of land productivity in the centerminous United States of America (CONUS) through machine learning algorithms using a data-driven approach to incorporate relationships from the data into the land productivity evaluation.