Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data

Irrigation is crucial to agriculture in arid and semi-arid areas and significantly contributes to crop development, food diversity and the sustainability of agro-ecosystems. For a specific crop, the separation of its irrigated and rainfed areas is difficult, because their phenology is similar and therefore less distinguishable, especially when there are phenology shifts due to various factors, such as elevation and latitude. In this study, we present a simple, but robust method to map irrigated and rainfed wheat areas in a semi-arid region of China. We used the Normalized Difference Vegetation Index (NDVI) at a 30 × 30 m spatial resolution derived from the Chinese HJ-1A/B (HuanJing(HJ) means environment in Chinese) satellite to create a time series spanning the whole growth period of wheat from September 2010 to July 2011. The maximum NDVI and time-integrated NDVI (TIN) that usually exhibit significant differences between irrigated and rainfed wheat were selected to establish a classification model using a support vector machine (SVM) algorithm. The overall accuracy of the Google-Earth testing samples was 96.0%, indicating that the classification results are accurate. The estimated irrigated-to-rainfed ratio was 4.4:5.6, close to the estimates provided by the agricultural sector in Shanxi Province. Our results illustrate that the SVM classification model can effectively avoid empirical thresholds in supervised classification and realistically capture the magnitude and spatial patterns of rainfed and irrigated wheat areas. The approach in this study can be applied to map irrigated/rainfed areas in other regions when field observational data are available

Effects of tillage systems in durum wheat under rainfed Mediterranean conditions

A 2-year conservation agriculture experiment was conducted in Southern Italy on durum wheat continuous cropping. Aim of the research was to assess the durum wheat productivity and grain quality in reduced soil tillage systems, according to conservation agriculture principles. The interactions among experimental treatments and climate revealed a close relationship among grain yield, grain quality and wheat growth conditions. Specifically, conventional tillage (CT, plowing and 2 disc harrowing) showed in the 2-year period higher grain production than reduced tillage treatments, minimum (MT, 1 disc harrowing) and No tillage (NT), especially for good crop water availability (3.29 t ha–1 of grain yield in CT, 2.67 in MT and 2.54 in NT). The amount of rainfall (above the average in both years) and its distribution in the growing seasons (more regular in the first year) strongly influenced wheat-grain quality indices (11.97% of protein content in the first year and 9.82% in the second one). Also, the wheat quality resulted more sensitive to the “Year × Tillage” interaction, with differences among tillages more evident in the second year and favourable to NT and MT. Spectral vegetation indexes (NDVI and TVI) measurements at flowering, have been shown to be useful to support farmers in N-late application for improving grain wheat quality. From this experiment carried out during the conversion period and in wet years, wheat managed with CT resulted in higher grain yield and quality, while only test weight showed a significant “Year × Tillage” interaction. Further indications emerged on the need to supply additional (10–20%) seed amount at sowing and crop nitrogen fertilizer in the first transition years in reduced tillage systems compared to conventional ones

A review of strategies, methods and technologies to reduce non-beneficial consumptive water use on farms considering the FAO56 methods

In the past few decades, research has developed a multitude of strategies, methods and technologies to reduce consumptive water use on farms for adaptation to the increasing incidence of water scarcity, agricultural droughts and multi-sectoral competition for water. The adoption of these water-saving practices implies accurate quantification of crop water requirements with the FAO56 crop coefficient approach, under diverse water availability and management practices. This paper critically reviews notions and means for maintaining high levels of water consumed through transpiration, land and water productivity, and for minimizing non-beneficial water consumption at farm level. Literature published on sound and quantified experimentation was used to evaluate water-saving practices related to irrigation methods, irrigation management and scheduling, crop management, remote sensing, plant conditioners, mulching, soil management and micro-climate regulation. Summary tables were developed on the benefits of these practices, their effects on non-beneficial water consumption, crop yields and crop water productivity, and the directions for adjustment of FAO56 crop coefficients when they are adopted. The main message is that on-farm application of these practices can result in water savings to a limited extent (usually<20%) compared to sound conventional practices, however this may translate into large volumes of water at catchment scale. The need to streamline data collection internationally was identified due to the insufficient number of sound field experiments and modelling work on the FAO56 crop water requirements that would allow an improved use of crop coefficients for different field conditions and practices. Optimization is required for the application of some practices that involve a large number of possible combinations (e.g. wetted area in micro-irrigation, row spacing and orientation, plant density, different types of mulching, in-field water harvesting) and for strategies such as deficit irrigation that aim at balancing water productivity, the economics of production, infrastructural and irrigation system requirements. Further research is required on promising technologies such as plant and soil conditioners, and remote sensing applicationsinfo:eu-repo/semantics/publishedVersio

Evaluating and Developing Methods for Non-Destructive Monitoring of Biomass and Nitrogen in Wheat and Rice Using Hyperspectral Remote Sensing

Aboveground plant biomass and plant nitrogen are two important parameters for plant growth monitoring, which have a decisive influence on the final yield. Mismanagement of fertilizer or pesticide inputs leads to poor plant growth, environmental pollution, and accordingly, yield loss. Biomass development is driven by nutrient supply, temperature, and phenology. Crop biomass reaches its highest weight at the harvest time. In contrast, plant nitrogen is dependent from fertilizer inputs to the soil and from biomass. Destructive measurement of both parameters is time-consuming and labor-intensive. Remote sensing offers remotely non-direct observation methods from outer space, air space, or close-range in the field by sensors. This dissertation focuses on non-destructive monitoring of plant biomass (the primary parameter) and plant nitrogen (the secondary parameter) using hyperspectral data from non-imaging field spectrometers and the imaging EO-1 Hyperion satellite. The study was conducted on two field crops: winter wheat of two growing seasons of the Huimin test site in the North China Plain; and rice of three growing seasons of the Jiansanjiang test site in the Sanjiang Plain of China. Study fields were set up in different spatial scales, from small experimental scale to large farmers' scale. Extensive field measurements were carried out, including both destructive measuring and non-destructive hyperspectral remote sensing of biomass and plant nitrogen. Besides, two years' Hyperion images were acquired at the Huimin test site. Four different approaches were used to develop the estimation models, which include: vegetation indices (VIs), band combinations, Optimum Multiple Narrow Band Reflectance (OMNBR) and stepwise Multiple Linear Regression (MLR), and derivatives of reflectance. Based on these four approaches, models were constructed, compared, and improved step by step. Additionally, a multiscale approach and a new VI, named GnyLi, were developed. Since experimental and farmers' fields were differently managed, several calibration and validation methods were tested and the field datasets were pooled. All tested approaches and band selections were greatly influenced by single growth stages. The broad band VIs saturated for both crops at the booting stage at the latest and were greatly outperformed by the narrow band VIs with optimized band combinations. Model applications from experimental to farmers' scale using the narrow bands measured by field spectrometers mostly failed due to the effects of different management practices and crop cultivars at both spatial scales. In contrast, the multiscale approach was successfully applied in winter wheat monitoring to transfer data and knowledge from field spectrometer measurements from the experimental scale to the farmers' field scale and the scale that is covered by the Hyperion imagery. The GnyLi and the Normalized Ratio Index (NRI) based on the optimized band combinations performed the best in the up-scaling process in the winter wheat study. In the rice study, MLR or OMNBR models based on 4–6 narrow bands better explained biomass variability compared to VIs based on broad bands and optimized band combinations. The models were more robust when data from different scales were pooled and then randomly divided into calibration and validation datasets. Additional model improvements were obtained using derivatives of reflectance. This dissertation evaluates different hyperspectral remote sensing approaches for non-destructive biomass and plant nitrogen monitoring, with the main focus on biomass estimation. The results and comparisons of different approaches revealed their potentials and limits. Development of new VIs, such as GnyLi, is advantageous due to the saturation problem of broad band VIs. However, the developed VIs need to be tested and improved for different crops and sites. Detection of optimized band combinations facilitates the development of new VIs, which are site-specific and crop-specific. MLR-based models may better explain the biomass variability; nevertheless, with more bands, they are prone to the issues of over-fitting and collinearity. Hence, no more than six bands were recommended to select from the hyperspectral data. Derivatives of reflectance were beneficial at the early growing season of rice when the canopy was strongly influenced by background signals from soil and water. However, their benefits were reduced when more bands were used

Crop residue, manure and fertilizer in dryland maize under reduced tillage in northern China: I grain yields and nutrient use efficiencies

The rapidly increasing population and associated quest for food and feed in China has led to increased soil cultivation and nitrogen (N) fertilizer use, and as a consequence to increased wind erosion and unbalanced crop nutrition. In the study presented here, we explored the long-term effects of various combinations of maize stover, cattle manure and nitrogen (N) and phosphorus (P) fertilizer applications on maize (Zea mays L.) yield and nutrient and water use efficiencies under reduced tillage practices. In a companion paper, we present the effects on nutrient balances and soil fertility characteristics. The ongoing factorial field trial was conducted at Shouyang Dryland Farming Experimental Station in northern China from 1993 onwards. The incomplete, determinant-optimal design comprised 12 treatments, including a control treatment, in duplicate. Grain yields and N, P, and potassium (K) uptakes and N, P and K use efficiencies were greatly influenced by the amount of rain during the growing season (GSR), and by soil water at sowing (SWS). There were highly significant interactions between GSR and added stover and manure, expressed in complex annual variations in grain yield and N, P and K use efficiencies. Annual mean grain yields ranged from 3,000 kg ha¿1 to 10,000 kg ha¿1 and treatment mean yields from 4,500 kg ha¿1 to 7,000 kg ha¿1. Balanced combination of stover (3,000¿6,000 kg), manure (1,500¿6,000 kg) and N fertilizer (105 kg) gave the highest yield. Stover and manure were important for supplying K, but the effects differed greatly between years. Overall mean N recovery efficiency (NRE) ranged from 28% to 54%, depending on N source. NRE in wet years ranged from 50% to 90%. In conclusion, balanced combinations of stover, manure and NP fertilizer gave the highest yield and NRE. Reduced tillage with adding stover and manure in autumn prior to ploughing is effective in minimizing labor requirement and wind erosion. The potentials of split applications of N fertilizer, targeted to the need of the growing crop (response farming), should be explored to further increase the N use efficiency

Crop modeling for assessing and mitigating the impacts of extreme climatic events on the US agriculture system

The US agriculture system is the world’s largest producer of maize and soybean, and typically supplies more than one-third of their global trading. Nearly 90% of the US maize and soybean production is rainfed, thus is susceptible to climate change stressors such as heat waves and droughts. Process-based crop and cropping system models are important tools for climate change impact assessments and risk management. As data- science is becoming a new frontier for agriculture growth, the incoming decade calls for operational platforms that use hyper-local growth monitoring, high-resolution real-time weather and satellite data assimilation and cropping system modeling to help stakeholders predict crop yields and make decisions at various spatial scales. The fundamental question addressed by this dissertation is: How crop and cropping system models can be “useful” to the agriculture production, given the recent advent of cloud computing and earth observatory power? This dissertation consists of four main chapters. It starts with a study that reviews the algorithms of simulating heat and drought stress on maize in 16 major crop models, and evaluates algorithm performances by incorporating these algorithms into the Agricultural Production Systems sIMulator (APSIM) and running an ensemble of simulations at typical farms from the US Midwest. Results show that current parameterizations in most models favor the use of daylight temperature even though the algorithm was designed for using daily mean temperature. Different drought algorithms considerably differed in their patterns of water shortage over the growing season, but nonetheless predicted similar decreases in annual yield. In the next chapter of climate change assessment study, I quantify the current and future yield responses of US rainfed maize and soybean to climate extremes with and without considering the effect of elevated atmospheric CO2concentrations, and for the first time characterizes spatial shifts in the relative importance of temperature, heat and drought stresses. Model simulations demonstrate that drought will continue to be the largest threat to rainfed maize and soybean production, yet shifts in the spatial pattern of dominant stressors are characterized by increases in the concurrent stress, indicating future adaptation strategies will have trade-offs between multiple objectives. Following this chapter, I presented a chapter that uses billion-scale simulations to identify the optimal combination of Genotype × Environment × Management for the purpose of minimizing the negative impact of climate extremes on the rainfed maize yield. Finally, I present a prototype of crop model and satellite imagery based within-field scale N sidedress prescription tool for the US rainfed maize system. As an early attempt to integrate advances in multiple areas for precision agriculture, this tool successfully captures the subfield variability of N dynamics and gives reasonable spatially explicit sidedress N recommendations. The prescription enhances zones with high yield potentials, while prevents over-fertilization at zones with low yield potentials

Modeling Cotton and Winter Wheat Growth and Yield Responses to Irrigation Management in the Texas High Plains and Rolling Plains

A significant portion of the intensively cultivated agricultural areas in the U.S. is located in the Texas High Plains and Rolling Plains. In recent years, decreasing ground water supplies and precipitation variability are presenting challenges for profitable cotton (Gossypium hirsutum L.) and wheat (Triticum aestivum L.) production in these regions. A field study was conducted in 2012 and 2013 at Chillicothe, TX, to investigate the growth, yield, and water use efficiency (WUE) responses of cotton cultivars under different irrigation and tillage treatments. Results revealed that deficit irrigation of 45% of cotton evapotranspiration (ET) increased the dryland yield and WUE by 260% and 39%, respectively. The irrigation-by-variety interaction showed that the 90% ET replacement treatment involving PHY375 produced the greatest lint yield and WUE. Tillage did not significantly affect lint yield, WUE, and fiber quality. Increasing irrigation resulted in a linear increase in fiber length and strength, and a linear decrease in fiber micronaire. Two vegetation indices, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were calculated using spectral reflectance dat. During the peak growing season, NDWI performed better compared to NDVI as no saturation problems were observed. The Crop Water Stress Index (CWSI) calculated using canopy and air temperature measurements showed significant differences among irrigation treatments. It was also observed that the CWSI and NDWI were negatively correlated. A modeling study was performed using the cotton growth simulation model, Cotton2K, to investigate the lint yield, WUE, and economic return responses using 31 years weather records (1980 – 2010) from the Texas Rolling Plains. Results revealed that replacing 112% ET maximized the yield while economic return was maximized at 108% ET. When water resources are limited, deficit irrigation at 80% ET replacement can be used to improve cotton WUE without significant yield and economic reductions in the semi-arid Texas Rolling Plains. A third study was performed on winter wheat using the DSSAT-CERES-Wheat model to investigate winter wheat growth and yield responses to irrigation management in the Texas High Plains using long-term weather datasets available for the Texas High Plains region (1980-2012). Results of winter wheat response to irrigation indicated that deficit irrigation between jointing and anthesis could significantly increase winter wheat grain yield and WUE. Application of 100 mm of irrigation at jointing and 120 mm at anthesis was found to produce a grain and biomass yields and WUE similar to full irrigation with significant amount of water saving

Spatial and Temporal Distribution of Groundwater Recharge in the West Bank Using Remote Sensing and GIS Techniques

Estimating groundwater recharge to aquifer systems is a very important element in assessing the water resources of the West Bank. Of particular interest is the sustainable yield of the aquifers. Previous studies have developed analytical recharge models that are based on the long-term annual rainfall data. These models have been shown to be inadequate and changes over shorter periods, e.g. monthly estimates, must be known in order to study the temporal distribution of recharge. The approach used in this research integrates data derived from satellite images (e.g. land cover, evapotranspiration, rainfall, and digital elevation model) with hydrogeological data in a Geographic Information System (GIS) model to identify and map the surface recharge areas. The Surface Energy Balance Algorithm for Land (SEBAL) is applied to time series of remote sensing MODerate Resolution Imaging Spectroradiometer (MODIS) level 3 data of reflectance and surface temperature measurements to estimate monthly evapotranspiration; precipitation is derived from the monthly data sets of the Tropical Rainfall Measuring Mission (TRMM); runoff is given assumed values of 0.75 mm month-1 and 0.4 mm month-1 for the months of January and February, respectively. Recharge is quantified from November until March by applying the water balance method where evapotranspiration estimates and runoff are subtracted from precipitation. Results show good agreement between data reported in the literature and remote sensing-based analysis. Empirical models that are based on long term rainfall measurements suggest recharge values between 800 and 836 MCM yr-1 while the remote sensing based model results estimate recharge to be 700 MCM yr-1. The Western, North-Eastern, and Eastern Aquifer Basins receive 30%, 23%, and 47% of the total calculated recharge while percentages available in the literature provide 49%, 22%, and 29%, respectively. Discrepancies are mainly due to lack of field data, the overestimation of actual evapotranspiration, and underestimation of TRMM precipitation values. The recharge map indicates that the most effective groundwater recharge zones are located in the north and west of the area that is characterised by thick and well developed soil deposits, heavy vegetation, and a sub-humid climate with the potential of significant recharge occurring during the wet season. Some areas in the east include concentration of drainage and stream flows which increase the ability of to recharge the groundwater system. The least effective areas are in the south and south-west region that is more arid with much less recharge, mainly due to its isolated thin soil deposits. A sensitivity analysis was carried out to demonstrate the impact of land cover change on groundwater and natural recharge. The assessment involved the use of land covers of 1994 and 2004 with the same fixed parameters of evapotranspiration, precipitation, drainage, slope, soil, and geology. Results show a decrease in high and intermediate high recharge areas from 40.25 km2 and 2462.25 km2 in year 1994 to 15.5 km2 and 1994 km2 in 2004, respectively. This illustrates the extent of land cover/land use change influence on recharge and calls for integrated plans and strategies to preserve recharge at least at its current rates