Towards quantification of the national water footprint in rice production of China: A first assessment from the perspectives of single-double rice.

Rice is one of the most important crops in China, contributing to approximately 28% of total cereal yield. Despite substantial production, given that rice is a high water-consuming crop, the water shortage due to the irreversible decline in available water resources on a global scale induced by undergoing climate change will pose grave challenges to rice reproductive growth and related water resources utilization. As a consequence, investigating the responses of rice productivity and water consumption to more pronounced climate changes is of great significance for water resources sustainable utilization in terms of reducing irrigation water requirements and ensuring food security. Present water footprint (WF) methods do not calculate the weighted average of each WF component at the national level when evaluating the effects of prospective climate change upon rice production. The national water footprint (NWF), i.e. taking the share of each province in the total production of crops as weighting factors, has been regarded as an effective approach to determine where each WF component is originally located. In this study, the temporal change characteristics of NWF for single-rice (SR), early-rice (ER) and late-rice (LR) in different agro-ecological zones across China during 2001-2010 were assessed for the first time. The results exhibited that NWF of rice was an estimated 304,848 million cubic meters (MCM) per year. The SR accounted for the greatest portion of NWF, followed by ER and LR. The NWF rank was SR-V > SR-I > ER-VI > SR-IV > LR-III > LR-VI > SR-II > ER-III. The blue water footprint (WFb) presents decreasing trends in most agro-ecological zones (SR-I, SR-II, SR-IV, ER-III and LR-VI), while green water footprint (WFg) exhibits increasing trends within these regions. This study provides a beneficial approach for decision-making processes aiming at better agricultural water resources management strategies to alleviate water resources scarcity and reduce food risk in the context of surging demand, which will support agricultural water resources management of China towards a more balanced direction at the national level.

Application of water footprint combined with a unified virtual crop pattern to evaluate crop water productivity in grain production in China.

Water shortages are detrimental to China's grain production while food production consumes a great deal of water causing water crises and ecological impacts. Increasing crop water productivity (CWP) is critical, so China is devoting significant resources to develop water-saving agricultural systems based on crop planning and agricultural water conservation planning. A comprehensive CWP index is necessary for such planning. Existing indices such as water use efficiency (WUE) and irrigation efficiency (IE) have limitations and are not suitable for the comprehensive evaluation of CWP. The water footprint (WF) index, calculated using effective precipitation and local water use, has advantages for CWP evaluation. Due to regional differences in crop patterns making the CWP difficult to compare directly across different regions, a unified virtual crop pattern is needed to calculate the WF. This project calculated and compared the WF of each grain crop and the integrated WFs of grain products with actual and virtual crop patterns in different regions of China for 2010. The results showed that there were significant differences for the WF among different crops in the same area or among different areas for the same crop. Rice had the highest WF at 1.39 m(3)/kg, while corn had the lowest at 0.91 m(3)/kg among the main grain crops. The WF of grain products was 1.25 m(3)/kg in China. Crop patterns had an important impact on WF of grain products because significant differences in WF were found between actual and virtual crop patterns in each region. The CWP level can be determined based on the WF of a virtual crop pattern, thereby helping optimize spatial distribution of crops and develop agricultural water savings to increase CWP.

Distribution and dynamics of Japanese beetles along the Indianapolis Airport perimeter and the influence of land use on trap catch.

As part of a program to minimize the accidental transportation of Japanese beetles (Popillia japonica) through cargo aircraft to areas where they are not established, a 4-yr trapping project was initiated to study the relative distribution and dynamics of the beetles along a trap line around the Indianapolis International Airport. Land use influence on beetle abundance (trap catch) was assessed using a geographic information system. Trap catch was consistently high in some locations and low in others. In general, high trap catches occurred near agronomic land planted with corn or soybeans, which are both preferred hosts of adult beetles. Low trap catches generally occurred in areas lacking preferred host plants. The amount of agronomic land within 500 m of the traps was always positively correlated with trap catch. Average trap catches were highly correlated by location from year to year, indicating stability of the relative distribution of the beetles along the trap line. Because high trap catches consistently occurred in the same locations, it can be inferred that trapping can be an effective method to monitor Japanese beetle populations. Taking airport-owned agronomic land out of corn and soybean production near the cargo terminals may reduce beetle activity in these areas.

Effects of calibration on L-THIA GIS runoff and pollutant estimation.

Urbanization can result in alteration of a watershed's hydrologic response and water quality. To simulate hydrologic and water quality impacts of land use changes, the Long-Term Hydrologic Impact Assessment (L-THIA) system has been used. The L-THIA system estimates pollutant loading based on direct runoff quantity and land use based pollutant coefficients. The accurate estimation of direct runoff is important in assessing water quality impacts of land use changes. An automated program was developed to calibrate the L-THIA model using the millions of curve number (CN) combinations associated with land uses and hydrologic soil groups. L-THIA calibration for the Little Eagle Creek (LEC) watershed near Indianapolis, Indiana was performed using land use data for 1991 and daily rainfall data for six months of 1991 (January 1-June 30) to minimize errors associated with use of different temporal land use data and rainfall data. For the calibration period, the Nash-Sutcliffe coefficient was 0.60 for estimated and observed direct runoff. The calibrated CN values were used for validation of the model for the same year (July 1-December 31), and the Nash-Sutcliffe coefficient was 0.60 for estimated and observed direct runoff. The Nash-Sutcliffe coefficient was 0.52 for January 1, 1991 to December 31, 1991 using uncalibrated CN values. As shown in this study, the use of better input parameters for the L-THIA model can improve accuracy. The effects on direct runoff and pollutant estimation of the calibrated CN values in the L-THIA model were investigated for the LEC. Following calibration, the estimated average annual direct runoff for the LEC watershed increased by 34%, total nitrogen by 24%, total phosphorus by 22%, and total lead by 43%. This study demonstrates that the L-THIA model should be calibrated and validated prior to application in a particular watershed to more accurately assess the effects of land use changes on hydrology and water quality.

Forecasting land use change and its environmental impact at a watershed scale.

Urban expansion is a major driving force altering local and regional hydrology and increasing non-point source (NPS) pollution. To explore these environmental consequences of urbanization, land use change was forecast, and long-term runoff and NPS pollution were assessed in the Muskegon River watershed, located on the eastern coast of Lake Michigan. A land use change model, LTM, and a web-based environmental impact model, L-THIA, were used in this study. The outcomes indicated the watershed would likely be subjected to impacts from urbanization on runoff and some types of NPS pollution. Urbanization will slightly or considerably increase runoff volume, depending on the development rate, slightly increase nutrient losses in runoff, but significantly increase losses of oil and grease and certain heavy metals in runoff. The spatial variation of urbanization and its impact were also evaluated at the subwatershed scale and showed subwatersheds along the coast of the lake and close to cities would have runoff and nitrogen impact. The results of this study have significant implications for urban planning and decision making in an effort to protect and remediate water and habitat quality of Muskegon Lake, which is one of Lake Michigan's Areas of Concern (AOC), and the techniques described here can be used in other areas.

Pork carcass composition derived from a neural network model of electromagnetic scans.

We used an advanced computer logic system (NETS 3.0) to decipher electromagnetic (EM) scans in lieu of traditional linear regression for estimation of pork carcass composition. Fifty EM scans of pork carcasses were obtained on-line (prerigor) at a swine slaughter facility. Right sides were cut into wholesale parts and dissected into fat, lean, and bone to obtain total dissected carcass and primal cut lean. In this study, the input layer consisted of 81 nodes (80-point EM scan curve and warm carcass weight), one hidden layer of 42 nodes, and an output layer consisting of one node, which were run separately for outputs of ham, loin, or shoulder lean. The hidden layer connected to the output of total lean contained 50 nodes. Thirty-five scans were used for training of the network. The new network was then tested with 15 previously unseen input/output pairs. Separate neural networks were developed for the estimation of dissected total carcass, ham, loin, and shoulder lean. The NETS configuration improved on linear regression equations for estimation of total carcass lean by .31 kg, ham lean by .284 kg, and shoulder lean by .148 kg. Our results show that advanced computer logic systems have the capacity to improve upon traditional linear regression equations for prediction of pork carcass composition.

Machine vision monitoring of plant health.

Techniques and algorithms to detect and diagnose disorders in plants grown in a controlled environment have been developed. A video camera senses features of plants which are indicative of disorders. Images are calibrated for size and color variations by using calibration templates. Different image segmentation techniques for separating object from background, have been implemented. Plant size and color properties have been investigated, temporal, spectral and spatial variation of leaves were extracted from the segmented images. Neural network and statistical classifiers were used to determine plant condition.