Yuxin Miao

Long-term experiments for sustainable nutrient management in China. A review

China is facing one of the largest challenges of this century to continue to increase annual cereal production to about 600 Mt by 2030 to ensure food security with shrinking cropland and limited resources, while maintaining or improving soil fertility, and protecting the environment. Rich experiences in integrated and efficient utilization of different strategies of crop rotation, intercropping, and all possible nutrient resources accumulated by Chinese farmers in traditional farming systems have been gradually abandoned and nutrient management shifted to over-reliance on synthetic fertilizers. China is now the world’s largest producer, consumer and importer of chemical fertilizers. Overapplication of nitrogen (N) is common in intensive agricultural regions, and current N-uptake efficiency was reported to be only 28.3, 28.2 and 26.1% for rice, wheat and maize, respectively, and less than 20% in intensive agricultural regions and for fruit trees or vegetable crops. In addition to surface and groundwater pollution and greenhouse gas emissions, over-application of N fertilizers has caused significant soil acidification in major Chinese croplands, decreasing soil pH by 0.13 to 2.20. High yield as a top priority, small-scale farming, lack of temporal synchronization of nutrient supply and crop demand, lack of effective extension systems, and hand application of fertilizers by farmers are possible reasons leading to the over-application problems. There is little doubt that current nutrient management practices are not sustainable and more efficient management systems need to be developed. A review of long-term experiments conducted around the world indicated that chemical fertilizer alone is not enough to improve or maintain soil fertility at high levels and the soil acidification problem caused by overapplication of synthetic N fertilizers can be reduced if more fertilizer N is applied as NO3− relative to ammonium- or urea-based N fertilizers. Organic fertilizers can improve soil fertility and quality, but long-term application at high rates can also lead to more nitrate leaching, and accumulation of P, if not managed well. Well-managed combination of chemical and organic fertilizers can overcome the disadvantages of applying single source of fertilizers and sustainably achieve higher crop yields, improve soil fertility, alleviate soil acidification problems, and increase nutrient-use efficiency compared with only using chemical fertilizers. Crop yield can be increased through temporal diversity using crop rotation strategies compared with continuous cropping and legume-based cropping systems can reduce carbon and nitrogen losses. Crop yield responses to N fertilization can vary significantly from year to year due to variation in weather conditions and indigenous N supply, thus the commonly adopted prescriptive approach to N management needs to be replaced by a responsive in-season management approach based on diagnosis of crop growth, N status and demand. A crop sensor-based in-season site-specific N management strategy was able to increase Nuptake efficiency by 368% over farmers’ practices in the North China Plain. Combination of these well-tested nutrient management principles and practices with modern crop management technologies is needed to develop sustainable nutrient management systems in China that can precisely match field-to-field and year-to-year variability in nutrient supply and crop demand for both single crops and crop rotations to not only improve nutrient-use efficiency but also increase crop yield and protect the environment. In addition, innovative and effective extension and service-providing systems to assist farmers in adopting and applying new management systems and technologies are also crucially important for China to meet the grand challenge of food security, nutrient-use efficiency and sustainable development.

Combining chlorophyll meter readings and high spatial resolution remote sensing images for in-season site-specific nitrogen management of corn

The chlorophyll meter (CM) has been commonly used for in-season nitrogen (N) management of corn (Zea mays L.). Nevertheless, it has limited potential for site-specific N management in large fields due to difficulties in using it to generate N status maps. The objective of this study was to determine how well CM readings can be estimated using aerial hyper-spectral and simulated multi-spectral remote sensing images at different corn growth stages. Two field experiments were conducted in Minnesota, USA during 2005 involving different N application rates and timings on a corn-soybean [Glycine max (L.) Merr.] rotation field and a corn-corn rotation field. Four flights were made during the growing season using the AISA Eagle Hyper-spectral Imager and CM readings were collected at four or five different growth stages. The results indicated that single multi-spectral and hyper-spectral band or vegetation index could explain 64–86% and 73–88% of the variability in CM readings, respectively, except at growth stage V9 in the corn-soybean rotation field where no band or vegetation index could explain more than 37% of the variability in CM readings. Multiple regression analysis demonstrated that the combination of 2–4 broad-bands or 3–8 narrow-bands could explain 41–92% or 61–94% of the variability in CM readings across the two fields and different corn growth stages investigated. It was concluded that the combination of CM readings with high spatial resolution hyper-spectral or multi-spectral remote sensing images can overcome the limitations of using them individually, thus offering a practical solution to N deficiency detection and possibly in-season site-specific N management in large continuous corn fields or at later stages in corn-soybean rotation fields.

Rice monitoring with multi-temporal and dual-polarimetric TerraSAR-X data

Abstract This study assesses the use of TerraSAR-X data for monitoring rice cultivation in the Sanjiang Plain in Heilongjiang Province, Northeast China. The main objective is the understanding of the coherent co-polarized X-band backscattering signature of rice at different phenological stages in order to retrieve growth status. For this, multi-temporal dual polarimetric TerraSAR-X High Resolution SpotLight data (HH/VV) as well as single polarized StripMap (VV) data were acquired over the test site. In conjunction with the satellite data acquisition, a ground truth field campaign was carried out. The backscattering coefficients at HH and VV of the observed fields were extracted on the different dates and analysed as a function of rice phenology to provide a physical interpretation for the co-polarized backscatter response in a temporal and spatial manner. Then, a correlation analysis was carried out between TerraSAR-X backscattering signal and rice biomass of stem, leaf and head to evaluate the relationship with different vertical layers within the rice vegetation. HH and VV signatures show two phases of backscatter increase, one at the beginning up to 46 days after transplanting and a second one from 80 days after transplanting onwards. The first increase is related to increasing double bounce reflection from the surface–stem interaction. Then, a decreasing trend of both polarizations can be observed due to signal attenuation by increasing leaf density. A second slight increase is observed during senescence. Correlation analysis showed a significant relationship with different vertical layers at different phenological stages which prove the physical interpretation of X-band backscatter of rice. The seasonal backscatter coefficient showed that X-band is highly sensitive to changes in size, orientation and density of the dominant elements in the upper canopy.

Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice

Abstract Appropriate field management requires methods of measuring plant height with high precision, accuracy, and resolution. Studies show that terrestrial laser scanning (TLS) is suitable for capturing small objects like crops. In this contribution, the results of multitemporal TLS surveys for monitoring plant height on paddy rice fields in China are presented. Three campaigns were carried out on a field experiment and on a farmer’s conventionally managed field. The high density of measurement points allows us to establish crop surface models with a resolution of 1 cm, which can be used for deriving plant heights. For both sites, strong correlations (each R 2 = 0.91 between TLS-derived and manually measured plant heights confirm the accuracy of the scan data. A biomass regression model was established based on the correlation between plant height and biomass samples from the field experiment ( R 2 = 0.86 ). The transferability to the farmer’s field was supported with a strong correlation between simulated and measured values ( R 2 = 0.90 ). Independent biomass measurements were used for validating the temporal transferability. The study demonstrates the advantages of TLS for deriving plant height, which can be used for modeling biomass. Consequently, laser scanning methods are a promising tool for precision agriculture.

Closing yield gaps in China by empowering smallholder farmers

Sustainably feeding the world’s growing population is a challenge, and closing yield gaps (that is, differences between farmers’ yields and what are attainable for a given region) is a vital strategy to address this challenge. The magnitude of yield gaps is particularly large in developing countries where smallholder farming dominates the agricultural landscape. Many factors and constraints interact to limit yields, and progress in problem-solving to bring about changes at the ground level is rare. Here we present an innovative approach for enabling smallholders to achieve yield and economic gains sustainably via the Science and Technology Backyard (STB) platform. STB involves agricultural scientists living in villages among farmers, advancing participatory innovation and technology transfer, and garnering public and private support. We identified multifaceted yield-limiting factors involving agronomic, infrastructural, and socioeconomic conditions. When these limitations and farmers’ concerns were addressed, the farmers adopted recommended management practices, thereby improving production outcomes. In one region in China, the five-year average yield increased from 67.9% of the attainable level to 97.0% among 71 leading farmers, and from 62.8% to 79.6% countywide (93,074 households); this was accompanied by resource and economic benefits.

Identifying important factors influencing corn yield and grain quality variability using artificial neural networks

Soil, landscape and hybrid factors are known to influence yield and quality of corn (Zea mays L.). This study employed artificial neural network (ANN) analysis to evaluate the relative importance of selected soil, landscape and seed hybrid factors on yield and grain quality in two Illinois, USA fields. About 7 to 13 important factors were identified that could explain from 61% to 99% of the observed yield or quality variability in the study site-years. Hybrid was found to be the most important factor overall for quality in both fields, and for yield as well in Field 1. The relative importance of soil and landscape factors for corn yield and quality and their relationships differed by hybrid and field. Cation exchange capacity (CEC) and relative elevation were consistently identified as among the top four most important soil and landscape factors for both corn yield and quality in both fields in 2000. Aspect and Zn were among the top five most important factors in Fields 1 and 2, respectively. Compound topographic index (CTI), profile curvature and tangential curvature were, in general, not important in the study site-years. The response curves generated by the ANN models were more informative than simple correlation coefficients or coefficients in multiple regression equations. We conclude that hybrid was more important than soil and landscape factors for consideration in precision crop management, especially when grain quality was a management objective.

Active canopy sensing of winter wheat nitrogen status

This paper systematically evaluated a three band active sensor, Crop Circle ACS-470.The GDVI index (R2=0.60) performed best for estimating plant N concentration.The CIG index (R2=0.89) performed best for estimating plant N uptake.The GRDVI and MGSAVI indices (R2=0.78 and 0.77) performed best for estimating NNI. Crop canopy sensor based in-season site-specific nitrogen (N) management is a promising approach to precision N management. GreenSeeker sensor has previously been evaluated in North China Plain (NCP) for improving winter wheat (Triticum aestivum L.) N management. The Crop Circle ACS-470 is an active canopy sensor with three user-configurable wavebands. This study identified important vegetation indices that can be calculated from Crop Circle green, red edge and near infrared (NIR) wavebands for estimating winter wheat N status and evaluated their potential improvements over GreenSeeker normalized difference vegetation index (NDVI) and ratio vegetation index (RVI). Six field experiments involving different N rates and varieties were conducted in the Quzhou Experiment Station of the China Agricultural University from 2009 to 2012. The results indicated that best Crop Circle ACS-470 sensor vegetation indices could explain similar amounts of aboveground biomass variability in comparison with GreenSeeker sensor NDVI, but Crop Circle normalized difference red edge/green optimized soil adjusted vegetation index (NDRE/GOSAVI) and red edge chlorophyll index (CIRE) were more sensitive to aboveground biomass (having lower noise equivalent) than GreenSeeker NDVI before and after biomass reached about 5000kgha-1, respectively. The Crop Circle green difference vegetation index (GDVI) (R2=0.60) and chlorophyll index (CIG) (R2=0.89) explained 53% and 7-11% more variability in plant N concentration and uptake than GreenSeeker indices, respectively. The Crop Circle green re-normalized difference vegetation index (GRDVI) (R2=0.78) and modified green soil adjusted vegetation index (MGSAVI) (R2=0.77) performed consistently better than GreenSeeker NDVI (R2=0.47) and RVI (R2=0.44) for estimating N nutrition index (NNI). We conclude that the three band user configurable Crop Circle ACS-470 sensor can improve the estimation of winter wheat N status as compared with two fixed band GreenSeeker sensor.

Spatial Variability of Soil Properties, Corn Quality and Yield in Two Illinois, USA Fields: Implications for Precision Corn Management

Better understanding of within-field spatial variability of crop quality parameters and yield are needed for precision management of crops. This study was conducted to determine the magnitude of within-field variability in soil properties, corn (Zea mays L.) quality parameters and yield and to characterize their spatial structures. Another objective was to compare the effects of hybrid on corn quality, yield, and the spatial structure of grain quality. Four Pioneer hybrids were planted side-by-side, two in each of the two study fields in eastern Illinois, USA. Coefficients of variation (CV%) for soil properties varied from 6.3 (pH) to 56.8% (soil test P). All the soil properties (except pH at Site 2) displayed well-defined spatial structures, with either strong or moderate spatial dependence. Variability in corn quality and yield (CVs < 10%) was smaller than variability in soil properties. Most quality parameters examined at Site 1 exhibited either moderate or strong spatial dependence, except that corn oil (both hybrids), kernel roundness and weight (hybrid 33Y18) did not show any spatial correlation. Hybrid 33G26 had significantly higher yield and quality for most quality parameters than 33Y18 at Site 1. At Site 2, hybrid 34W67 was significantly lower in oil and protein content, length, roundness and vitreousness than 34K77, but higher in other quality parameters. Significant differences in spatial structures were also observed across hybrids for some corn quality parameters. We conclude that hybrid selection is an important strategy for precision management of corn for optimum yield and quality.

In-Season Estimation of Rice Nitrogen Status With an Active Crop Canopy Sensor

Timely nondestructive estimation of crop nitrogen (N) status is crucial for in-season site-specific N management. Active crop canopy sensors are the promising tools to obtain the needed information without being affected by environmental light conditions. The objective of this study was to evaluate the potential for the GreenSeeker active crop canopy sensor to estimate rice (Oryza sativa L.) N status. Nine N rate experiments were conducted from 2008 to 2012 in Jiansanjiang, Heilongjiang Province in Northeast China. The results indicated that across site-years and growth stages, normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) obtained with the GreenSeeker sensor could explain 73%-76% and 70%-73% of rice aboveground biomass and plant N uptake variability in this study, respectively. The NDVI index became saturated when biomass reached about 4 t ha-1 or when plant N uptake reached about 100 kg ha-1, whereas RVI did not show obvious saturation effect. The validation results, however, indicated that both indices performed similarly, and their relative errors (RE) were still large (> 40%). Although the two indices only explained less than 40% of plant N concentration or N nutrition index (NNI) variability, the RE values were acceptable (<; 26%). The results indicated some potentials of using the GreenSeeker sensor to estimate rice N status nondestructively, but more studies are needed to further evaluate and improve its performance for practical applications.

Best Accuracy Land Use/Land Cover (LULC) Classification to Derive Crop Types Using Multitemporal, Multisensor, and Multi-Polarization SAR Satellite Images

When using microwave remote sensing for land use/land cover (LULC) classifications, there are a wide variety of imaging parameters to choose from, such as wavelength, imaging mode, incidence angle, spatial resolution, and coverage. There is still a need for further study of the combination, comparison, and quantification of the potential of multiple diverse radar images for LULC classifications. Our study site, the Qixing farm in Heilongjiang province, China, is especially suitable to demonstrate this. As in most rice growing regions, there is a high cloud cover during the growing season, making LULC from optical images unreliable. From the study year 2009, we obtained nine TerraSAR-X, two Radarsat-2, one Envisat-ASAR, and an optical FORMOSAT-2 image, which is mainly used for comparison, but also for a combination. To evaluate the potential of the input images and derive LULC with the highest possible precision, two classifiers were used: the well-established Maximum Likelihood classifier, which was optimized to find those input bands, yielding the highest precision, and the random forest classifier. The resulting highly accurate LULC-maps for the whole farm with a spatial resolution as high as 8 m demonstrate the beneficial use of a combination of x- and c-band microwave data, the potential of multitemporal very high resolution multi-polarization TerraSAR-X data, and the profitable integration and comparison of microwave and optical remote sensing images for LULC classifications.