Abstract
While most land use and land cover (LULC) studies have focused on modeling, change detection and driving forces at the class or categorical level, few have focused on the subclass level, especially regarding the quality change within a class such as farmland. The concept of nondominant farmland area (NAF) is proposed in this study to assess within class variability and quantify farmland areas where poor environmental conditions, unsuitable natural factors, natural disasters or unsustainable management practices lead to poor crop growth and thus low yield. A 17-year (2000–2016) time series of the Normalized Difference Vegetation Index (NDVI) was used to develop a NAF extraction model with abnormal features in the NDVI curves and subsequently applied to Heilongjiang province in China. The NAF model was analyzed and assessed from three aspects: agricultural disasters, soil types and medium- and low-yield fields, to determine dominant factors of the NAF patterns. The results suggested that: (1) the NAF model was able to extract a variety of NAF types with an overall accuracy of ~80%. The NAF area accumulated more than 8 years in 17 years is 6.20 thousand km2 in Heilongjiang Province, accounting for 3.75% of the total cultivated land area; (2) the NAF had significant spatial clustering characteristics and temporal variability. 53.24% of the NAF accumulated more than 8 years in 17 years is mainly concentrated in the west of Heilongjiang Province. The inter-annual NAF variability was related with meteorological variations, topography and soil properties; and (3) the spatial and temporal NAF patterns seem to reflect a cumulative impact of meteorological disasters, poor farmland quality, and soil degradation on crop growth. The determinant factors of the observed NAF patterns differed across regions, and must be interpreted in the local context of topography, soil properties and meteorological environment. Spatial and temporal NAF variability could provide useful, diagnostic information for precision farmland management.
Highlights
The world’s population has reached 7.7 billion (United Nations [1]), and there is a substantial demand for food but there is a limited amount of reclaimable farmland
The GMIN values of DOY 185 and 193 of the 3 farms located in different phenological regions are 0.648, 0.773, 0.728, 0.791, 0.694 and 0.752 calculated by Equation. (2), which is the threshold of model in 2016 (Figure 3)
The nondominant farmland area (NAF) proportions on the southwest of Songnen Plain, including Durbert, Lindian, Daqing, Tailai, Anda, Gannan and Fuyu county, were more than 15%. 53.24% of the NAF accumulated more than 8 years in 17 years is mainly concentrated in the west of Heilongjiang Province, due to mainly located in the aeolian sandy soil region that have a poor resistance to drought and flooding
Summary
The world’s population has reached 7.7 billion (United Nations [1]), and there is a substantial demand for food but there is a limited amount of reclaimable farmland. Increasing the yields of middle- and low-yield farmland is probably the most feasible means to grow more food to meet the increasing demand. Middleand low-yield farmland areas are generally extracted using an evaluation index system of farmland quality [3,4]. These evaluation index systems take little into account of crop growth dynamics and, higher farmland ranks do not always translate into high yields, due to the suitability of crop varieties and other factors. The concept of nondominant farmland area (NAF) is proposed, referring to areas with poor environmental conditions, uncoordinated crop environmental factors and disaster-prone farmland leading to poor crop growth and a low yield. The extraction of the NAF is conducive to the evaluation of farmland quality, macroscopic management, targeted improvements and farmland protection, which could provide scientific guidance for crop planting structure adjustments, disaster monitoring, agricultural production management and macro decision making
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