基于STM32的连栋温室精准灌溉控制系统

以STM32F103芯片为控制核心,选用FDR型土壤水分传感器测定作物当前生长期的土壤水分含量,结合当前作物所需要的土壤含水量,实时计算出差值,给出当前作物所需要的灌溉量,通过PC机给出灌溉命令,控制恒压变频柜的运行和灌溉电磁阀的开关。同时控制器能将变频器和各个电磁阀的运行状态、当前灌溉量和实时的土壤含水量传输给PC机。该系统不仅能精确的自动控制灌溉,而且可以根据种植者的要求提供灌溉。该系统能实现多个温室的精准灌溉自动控制,具有结构简单、成本低和可靠性高,对于实现节水灌溉和发展高效农业具有指导意义。     

Design of decision model for sensitive crop irrigation system

Agriculture Industry is highly dependent on environmental and weather conditions. Many times, crops are spoiled because of sudden changes in weather. Therefore, we need a decision model to take care the water requirement of sensitive crops of agriculture industry. The proposed work presents a novel and proficient hybrid model for sensitive crop irrigation system (SCIS). For implementation of the model, brassica crop is taken. The duration and amount of water to be supplied is based upon the weather prediction and soil condition information. The decision model is developed using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) for brassica crops. In this model, if the input data values are available in range, then ANFIS model would be preferred and if the data sets are available for training, testing and validation then ANN model would be the best choice. The soil moisture, soil status in terms of temperature and leaf wetness are the input and flow control of sprinklers is the out for SCIS. The predicted outputs are analysed to assert the suitability of the proposed approach in the brassica crops. The proposed SCIS achieved an accuracy of 91% and 99% for ANFIS and ANN models respectively.     

AquaCrop: FAO's crop water productivity and yield response model

With the help of relatively few conservative crop parameters, AquaCrop simulates final crop yield in four steps that are easy to understand, which makes the modeling approach transparent. The steps consist in the simulation of development of the green crop canopy cover, crop transpiration, above-ground biomass, and final crop yield. Temperature and water stresses directly affect one or more of the above processes. Nutrient deficiencies and salinity effects are simulated indirectly by moderating canopy cover development over the season, and by reducing crop transpiration and the normalized water productivity. The effect of CO₂ concentration on biomass is simulated by altering the normalized water productivity. The model requires a relatively small number of explicit parameter values and mostly intuitive input variables. The paper describes the essence of AquaCrop Version 4.0, applications and parameterization of crops, crop responses to elevated CO₂ concentration, soil fertility and salinity, and further model developments.     

Bridging rigorous assessment of water availability from field to catchment scale with a parsimonious agro-hydrological model

While simple crop and hydrological models are limited with respect to the number and accuracy of the processes they incorporate, complex models have high demand for data. Due to the limitations of both categories of models, there is a need for new agro-hydrological models that simulate both crop productivity and water availability in agricultural catchments, with low data and calibration requirements. This study aimed at developing a widely applicable parsimonious agro-hydrological model, AquaCrop-Hydro, which couples the AquaCrop crop water productivity model with a conceptual hydrological model. AquaCrop-Hydro, simulating crop productivity, the daily soil water balance and discharge at the catchment outlet, performed well for an agricultural catchment in Belgium. The model can be used to investigate the effect of agricultural management and environmental changes from field to catchment scale in support of sustainable water management in agricultural areas.     

基于NB-IOT的大田管理精准农业系统的设计与实现

针对传统规模化大田种植缺乏科学监测手段,农作物数据采集系统存在智能化程度不高、管理效率低下,难以实现对大田作物精准灌溉的问题,以及当前成熟商用的物联网通信技术存在低功耗与广覆盖难以两全的问题,开发出一个基于NB-IOT的大田管理精准农业系统,给出了系统的总体设计方案,详细阐释了系统的硬件与软件设计方法。该系统利用无线传感器节点采集作物的地面气象信息、土壤信息,以及作物的生长实况信息,通过NB-IOT传送到云服务器,实现对大田作物生长环境的远程监控和精准灌溉,在提高大田作物的智能化管理水平方面具有良好的推广前景。     

Potential use of saline groundwater for irrigation in the Murray hydrogeological basin of Australia

Scarce surface water resources have led farmers to use groundwater heavily for irrigation in the Murray-Darling Basin of Australia. Saline groundwater is emerging as an alternative source of water for irrigation. This study examines the potential use of saline groundwater for a range of crops. Among cropping groups modelled, oilseeds and grain crops are considerably tolerant to saline groundwater in terms of the change yield with salinity levels, although the tolerance levels are crop-specific. Based on availability of saline groundwater, coarse textured soil, deep water table and moderate rainfall, this study also revealed that twenty-two percent or seven million hectares of the Murray hydrogeological basin in the southern Murray-Darling Basin may be suitable for the saline groundwater irrigation. However, it is also noted that the use of saline groundwater is only feasible for saline-tolerant crops under proper drainage management and by observing suitable precautionary measures. Therefore, the use of saline groundwater in irrigation requires careful attention to monitor the build up of salt in the root zone.     

Accuracy,robustness and behavior of the STICS soil–crop model for plant,water and nitrogen outputs: Evaluation over a wide range of agro-environmental conditions in France

Soil–crop models are increasingly used as predictive tools to assess yield and environmental impacts of agriculture in a growing diversity of contexts. They are however seldom evaluated at a given time over a wide domain of use. We tested here the performances of the STICS model (v8.2.2) with its standard set of parameters over a dataset covering 15 crops and a wide range of agropedoclimatic conditions in France. Model results showed a good overall accuracy, with little bias. Relative RMSE was larger for soil nitrate (49%) than for plant biomass (35%) and nitrogen (33%) and smallest for soil water (10%). Trends induced by contrasted environmental conditions and management practices were well reproduced. Finally, limited dependency of model errors on crops or environments indicated a satisfactory robustness. Such performances make STICS a valuable tool for studying the effects of changes in agro-ecosystems over the domain explored.     

Crop classification modelling using remote sensing and environmental data in the Greater Platte River Basin,USA

With an ever expanding population, potential climate variability and an increasing demand for agriculture-based alternative fuels, accurate agricultural land-cover classification for specific crops and their spatial distributions are becoming critical to researchers, policymakers, land managers and farmers. It is important to ensure the sustainability of these and other land uses and to quantify the net impacts that certain management practices have on the environment. Although other quality crop classification products are often available, temporal and spatial coverage gaps can create complications for certain regional or time-specific applications. Our goal was to develop a model capable of classifying major crops in the Greater Platte River Basin (GPRB) for the post-2000 era to supplement existing crop classification products. This study identifies annual spatial distributions and area totals of corn, soybeans, wheat and other crops across the GPRB from 2000 to 2009. We developed a regression tree classification model based on 2.5 million training data points derived from the National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) in relation to a variety of other relevant input environmental variables. The primary input variables included the weekly 250 m US Geological Survey Earth Observing System Moderate Resolution Imaging Spectroradiometer normalized differential vegetation index, average long-term growing season temperature, average long-term growing season precipitation and yearly start of growing season. An overall model accuracy rating of 78% was achieved for a test sample of roughly 215 000 independent points that were withheld from model training. Ten 250 m resolution annual crop classification maps were produced and evaluated for the GPRB region, one for each year from 2000 to 2009. In addition to the model accuracy assessment, our validation focused on spatial distribution and county-level crop area totals in comparison with the NASS CDL and county statistics from the US Department of Agriculture (USDA) Census of Agriculture. The results showed that our model produced crop classification maps that closely resembled the spatial distribution trends observed in the NASS CDL and exhibited a close linear agreement with county-by-county crop area totals from USDA census data (R ² = 0.90).     

Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains

The global environmental change research community requires improved and up-to-date land use/land cover (LULC) datasets at regional to global scales to support a variety of science and policy applications. Considerable strides have been made to improve large-area LULC datasets, but little emphasis has been placed on thematically detailed crop mapping, despite the considerable influence of management activities in the cropland sector on various environmental processes and the economy. Time-series MODIS 250 m Vegetation Index (VI) datasets hold considerable promise for large-area crop mapping in an agriculturally intensive region such as the U.S. Central Great Plains, given their global coverage, intermediate spatial resolution, high temporal resolution (16-day composite period), and cost-free status. However, the specific spectral-temporal information contained in these data has yet to be thoroughly explored and their applicability for large-area crop-related LULC classification is relatively unknown. The objective of this research was to investigate the general applicability of the time-series MODIS 250 m Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) datasets for crop-related LULC classification in this region. A combination of graphical and statistical analyses were performed on a 12-month time-series of MODIS EVI and NDVI data from more than 2000 cropped field sites across the U.S. state of Kansas. Both MODIS VI datasets were found to have sufficient spatial, spectral, and temporal resolutions to detect unique multi-temporal signatures for each of the region's major crop types (alfalfa, corn, sorghum, soybeans, and winter wheat) and management practices (double crop, fallow, and irrigation). Each crop's multi-temporal VI signature was consistent with its general phenological characteristics and most crop classes were spectrally separable at some point during the growing season. Regional intra-class VI signature variations were found for some crops across Kansas that reflected the state's climate and planting time differences. The multi-temporal EVI and NDVI data tracked similar seasonal responses for all crops and were highly correlated across the growing season. However, differences between EVI and NDVI responses were most pronounced during the senescence phase of the growing season.     

Optimum control of irrigation water application

The problem of controlling soil water within the root zone of irrigated crops to minimize the expected loss is examined. Control is accomplished by scheduling the amount of and timing irrigations to replenish the soil water reservoir depleted by the crop's water consumption. Actual evapo-transpiration rates are a function of the prevailing soil water level and the evaporative demand, which may be considered to be either deterministic or probabilistic. For crops grown on a particular soil, an optimum soil water level is defined as the lowest soil water level above which crops are not stressed. The reduced yield of a crop is related to its growth stage and to the amount and duration that the soil water content is below this optimum value.Existing inventory models are adapted for the purpose of determining the optimal irrigation policy, that is, the timing and amount of water application that result in the minimum irrigation cost to the farmer. Solutions to complex decision-making problems are currently available for a variety of irrigation situations.     

IRRIGATE: A dynamic integrated model combining a knowledge-based model and mechanistic biophysical models for border irrigation management

Water management practices in southern France (the Crau plain) need to be modified in order to ensure greater water use efficiency and less environmental damage while maintaining hay production levels. Farmers, water managers and policy makers have expressed the need for new methods to deal with these issues. We developed the biodecisional model IRRIGATE to test new irrigation schedules, new designs for water channels or fields and new distribution planning for a given water resource. IRRIGATE simulates the operation of a hay cropping system irrigated by flood irrigation and includes three main features: (i) border irrigation with various durations of irrigation events and various spatial orders of water distribution, (ii) species-rich grasslands highly sensitive to water deficit, (iii) interactions between irrigation and mowing. It is based on existing knowledge, adapted models and new modules based on experiments and survey data. It includes a rule-based model on the farm scale, simulating dynamically both irrigation and mowing management, and two biophysical models. The two biophysical models are a dynamic crop model on the field scale simulating plant and soil behaviour in relation to water supply, and a flood irrigation model on the border scale simulating an irrigation event according to plant and hydraulic parameters. Model outputs allow environmental (water supply, drainage), social (labour) and agronomic (yields, water productivity and irrigation efficiency) analyses of the performance of the cropping system. IRRIGATE was developed using firstly a conceptual framework describing the system modelled as three sub-systems (biophysical, technical, and decision) interacting within the farm. Then a component-based spatially explicit modelling based on the identification of the interactions between modules, the identification of temporal and spatial scales of modules and the re-use of previous models was used to develop the numerical model. An example of the use of the biodecisional model is presented showing the effects on a real farm of a severe water shortage in 2006.     

The implementation of a wateruse model to calculate radar backscatter with application to irrigation monitoring

This letter describes a coupled water use and radar backscatter model designed to assist irrigation monitoring and scheduling. The three components of the model (soil, plant, radar backscatter) are presented and simulations with the model explore its effectiveness in estimating soil and crop canopy moisture for potato crops by comparison with measurements obtained for test fields in Cambridgeshire, England, UK.     

Assessing the propagation of uncertainties in multi-objective optimization for agro-ecosystem adaptation to climate change

It is widely acknowledged that uncertainty needs to be accounted for in climate impact studies, be it in scenario analyses or optimization applications. In this study we investigate how climate and crop model uncertainties affect multi-objective optimization outputs aiming to identify optimum agricultural management adaptations for Western Switzerland. Results are visualized by ternary plots that map optimum management measures, crop yield, erosion and leaching with associated uncertainties for navigating through the optimum adaptation space. We find that the relevance of climate model vs. parameter uncertainty can differ substantially depending on the prioritization of objectives and local conditions. The optimum choice of irrigation level was found to be the decision variable subject to greatest uncertainty particularly on coarser soil. This finding suggests that for the long-term planning of irrigation infrastructure and management, a robust adaptation approach is required for approaching unavoidable uncertainty from a risk management perspective.     

Neural network and crop residue index multiband models for estimating crop residue cover from Landsat TM and ETM+ images

Crop residues on the soil surface provide not only a barrier against water and wind erosion, but they also contribute to improving soil organic matter content, infiltration, evaporation, temperature, and soil structure, among others. In Argentina, soybean (Glycine max (L.) Merill) and corn (Zea mays L.) are the most important crops. The objective of this work was to develop and evaluate two different types of model for estimating soybean and corn residue cover: neural networks (NN) and crop residue index multiband (CRIM) index, from Landsat images. Data of crop residue were acquired throughout the summer growing season in the central plains of Córdoba (Argentina) and used for training and validating the models. The CRIM, a linear mixing model of composite soil and residue, and the NN design, included reflectance and digital numbers from a combination of different TM bands to estimate the fractional residue cover. The results show that both methodologies are appropriate for estimating the residue cover from Landsat data. The best developed NN model yielded R² = 0.95 when estimating soybean and corn residue cover fraction, whereas the best fit using CRIM yielded R² = 0.87; in addition, this index is dependent on the soil and residue lines considered.     

Remote sensing and GIS for estimation of irrigation crop water demand

Satellite images supported by global positioning systems (GPS) and field visits were used to identify the cropping pattern of a large irrigation scheme in Central Asia. Two methods were used to estimate the crop evapotranspiration (ET). In the first, the ETs of the different crops were calculated from local field climatic data using the Penman–Monteith method of calculating crop water requirements as used in the Food and Agriculture Organization (FAO) CropWat programme. The satellite data were transferred to a geographical information system (GIS) and the area of each crop type was identified. Combining the two sets of data gave an estimate of ET and total evaporative water demand for each crop. ET was also calculated directly from the satellite data using a modified sensible heat flux approach (SEBAL). The Penman–Monteith approach estimated the ET to be 5.7, 3.3, 4.4 and 6.3?mm?d^?1 for cotton, mixed crop, alfalfa and rice respectively, whereas the ET estimated from the satellite data were 4.4, 3, 3.2 and 5.3?mm?d^?1, respectively. The possible causes of these differences are discussed. The FAO Penman–Monteith methodology for estimating crop water requirements is best for planning purposes but the SEBAL approach is potentially more useful for management in that it establishes the amount of water being used by the crop and can help identify where water is being wasted.     

Airborne multi-spectral monitoring of agricultural crop status: effect of time of year,crop type and crop condition parameter

Airborne optical multispectral imagery was acquired in conjunction with contemporaneous ground-based measurements of various crops (leaf area index, canopy temperature, plant height) at a test site in southern Alberta, Canada. Data were acquired on three occasions in July 1994 for a variety of crops and irrigation practices. A large number of crop condition-spectral relations were examined to determine whether the imagery could be used to measure the various crop condition parameters. It was found that a number of statistically significant correlations exist between the imagery and the crop condition parameters and that these correlations vary as a function of crop type, time of year, and crop condition parameter. The results suggest that in many cases, multi-spectral optical imagery can be used to monitor variations in crop condition parameters across the growing season for a variety of crop types.     

Sensitivity and uncertainty propagation in coupled models for assessing smallholder farmer food security in the Olifants River Basin,South Africa

Using family balance (i.e., combined net farm and non-farm incomes less family expenses), an output from an integrated model, which couples water resource, agronomic and socio-economic models, its sensitivity and uncertainty are evaluated for five smallholder farming groups (A–E) in the Olifants Basin. The crop management practiced included conventional rainfed, untied ridges, planting basins and supplemental irrigation. Scatter plots inferred the most sensitive variables affecting family balance, while the Monte Carlo method, using random sampling, was used to propagate the uncertainty in the model inputs to produce family balance probability distributions. A non-linear correlation between in-season rainfall and family balance arises from several factors that affect crop yield, indicating the complexity of farm family finance resource-base in relation to climate, crop management practices and environmental resources of soil and water. Stronger relationships between family balance and evapotranspiration than with in-season rainfall were obtained. Sensitivity analysis results suggest more targeted investment effort in data monitoring of yield, in-season rainfall, supplemental irrigation and maize price to reduce family balance uncertainty that varied from 42% to 54% at 90% confidence level. While supplemental irrigation offers the most marginal increase in yields, its wide adoption is limited by availability of water and infrastructure cost.     

Evaluation of spectral vegetation indices as an indicator of crop coefficient and evapotranspiration under full and deficit irrigation conditions

Two separate field experiments were conducted with sugar beet and green bean, at Ankara, Turkey during the 2005 growing season. Different amounts of irrigation water were applied, and various levels of water stress and vegetation occurred. Spectral reflectance, infrared canopy temperature, and some parameters related to crop evapotranspiration (ET _c) were observed. Daily ET _c values were calculated based on energy balance and soil water balance residual. The fraction of reference ET (ETrF), which is essentially the same with the crop coefficient (K _c), was determined, and relationships between spectral vegetation indices (SVIs) were analysed. Under water stress conditions, the ET _c and ETrF values estimated by means of energy balance were relatively high. In order to improve the correlation between ETrF and SVIs and for correction of ET _c for water‐stressed irrigation treatments, a modification ratio was calculated based on SVIs. Although all three SVIs have a significant relationship with ETrF, the correctness of the modification with a Simple Ratio (SR) was higher. As a consequence, ETrF or crop coefficient (K _c) could be estimated by SR, and this information could be used for irrigation water management of large‐scale agricultural lands.     

Potentiality of Landsat,SPOT and IRS satellite imagery,for recognition of salt affected soils in Indian Arid Zone

Landsat, SPOT and IRS data, black and white and false colour composite (FCC) imagery of the summer (April, May), rainfed crop season (October) and winter irrigated crop season (January, February) of Indian Arid Zone were interpreted for recognition or three types of salt affected soils, viz. (1) natural salt affected; slight, moderate and severe, (2) saline soils due to saline water irrigation, (3) sodic soils due to high residual sodium carbonate (RSC) water irrigation. These were field checked and supported by analytical data. The Landsat-MSS band 4 could only provide the overall extent of salinity. The moderate and severe natural salt affected soils were identified by Landsat-MSS band 2, Landsat-MSS and TM, IRS LISS-I and LISS-II and SPOT HRV2 data for April and January. But the differentiation between the saline and sodic soils was possible only by the use of multi-date imagery (October and January) and the clue provided by the cropping pattern. The potentiality of remote sensing data products for identification of the types and degree of salt affected soils is discussed.     

作物吸氮效率的试验研究

考虑灌水施肥两方面的影响因素 ,在试验资料的基础上 ,本文初步探讨了水、肥与作物产量的关系 ,分析了作物吸氮效率的内涵 ,建立了单因素氮素生产函数、双因素水肥生产函数 ,为北方缺水地区制定节水灌溉与科学施肥提供了依据。