In order to improve the maize yield in different management zones and achieve precision agricultural management within a large-scale field, correlated component regression (CCR) was used to screen limiting factors of maize yield from topographical attributes (elevation), soil physical factors (sand, silt, clay, bulk density), and initial soil properties (soil organic carbon, total nitrogen, total phosphorus, soil water content, available nitrogen, electrical conductivity). Yield estimation model was established based on yield-limiting factors in each management zone and the whole field. Management zones were delineated by using the fuzzy c-means clustering algorithm (FCM) based on the spatial variation of soil properties. For soil properties, statistically significant differences in most cases were found among different management zones (M1, M2, M3), excepted elevation, silt, and clay. The decrease in the coefficient of variation (CV) of soil properties in the management zones indicated that the distribution of soil properties was more homogeneous than in the whole field. Spatial distribution of yield and management zones were similar, while yield was significantly different in three management zones (M1>M2>M3). The inhomogeneous spatial distribution of soil properties showed that the limiting factors of yield could be varied among management zones. Therefore, the main purpose of this study was to find out the yield-limiting factors, establish yield estimation models based on yield-limiting factors, and find ways to improve the yield in each management zone within a field. Four correlated components (CC1~CC4) were obtained in management zones and the whole field by CCR. The factors which had largely standardized loadings (absolute value of standard loadings was greater than 0.2) on major correlated components (values of standardized weights were greater than 0.7) were identified as the main limiting factors of maize yield in zones. Yield in three management zones was measured and the limiting factors of yield in different zones were evaluated. The results showed that limiting factors for yield were silt, sand, soil organic carbon (SOC), soil moisture content (SWC), available nitrogen (AN), and total nitrogen (TN) in the whole field, which was different from management zones. The limiting factors of M1 were silt, sand, clay, AN, electrical conductivity (EC), TN, and total phosphorus (TP). Limiting factors of M2 were silt, sand, SWC, while the limiting factors were elevation, sand, clay, and EC for M3. Different yield estimation models were established by using CCR in management zones and the whole field. For model estimation, the correlation between simulated and measured yield was high, with R2=0.75 and nRMSE=0.20 in the whole field; in management zones, higher simulation accuracy was found: R2 of yield estimation model was 0.91, 0.84, and 0.76, while nRMSE were 0.15, 0.14, and 0.16 in Z1, Z2, and Z3, respectively. For model validation, R2 of yield estimation model was 0.70, 0.83, 0.78, and 0.71, while nRMSE were 0.21, 0.16, 0.18, and 0.17 in the whole field, Z1, Z2, and Z3, respectively. According to the results, different ways of improving yield were found. For the whole field, soil amelioration and fertilizer application before sowing were the keys to increase yield. The application of organic fertilizer and phosphorus fertilizer, reduction of soil EC, and the improvement of soil water holding capacity, were conducive to the improvement of yield in M1. Because soil texture and SWC were the main factors limiting the yield, improving soil water holding characteristics was also the way to increase yield in M2. For M3, irrigation before sowing could decrease EC of surface soil and improve soil water storage, which was beneficial to the emergence and growth of maize. Organic fertilizer application should also be considered for yield improvement in M3. Distributed management should be adopted based on the limiting factors of maize yield in management zones, while could be more targeted to improve crop yield within a field.