Image masking for crop yield forecasting using AVHRR NDVI time series imagery

Time series of the Advanced Very High-Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) have been used for crop yield forecasting since the 1980s. Image masking was a critical component of several of these yield forecasting efforts as researchers attempted to isolate subsets of a region’s pixels that would improve their modeling results. Approaches generally sought to identify cropped pixels and when possible, pixels that corresponded to the particular crop type under investigation. In this paper, the former approach will be referred to as cropland masking, and the latter approach will be called crop-specific masking.
The research presented in this paper examines the underlying assumptions made when image masking for the purpose of regional crop yield forecasting. An alternative statistical image masking approach (called yield-correlation masking) is proposed that is objective (i.e., can be automated) and has the flexibility to be applied to any region with several years of time series imagery and corresponding historical crop yield information. The primary appeal of yield-correlation masking is that, unlike cropland masking, no land cover map is required, yet we will show that NDVI models generated using the two methods demonstrate comparable predictive ability.

The goal of this research is very specific: We will establish the yield-correlation masking procedure as a viable image masking technique in the context of crop yield forecasting. To accomplish this objective, we empirically evaluate and compare cropland masking and yield-correlation masking for the purpose of crop yield forecasting. Cropland masking has been shown to benefit yield forecasting models, thus providing a practical benchmark. In the process, we present a robust statistical yield forecasting protocol that can be applied to any (region, crop)-pair possessing the requisite data, and this protocol is used to evaluate the two masking methods that are being compared.
This paper is developed as follows. A brief review of related research is presented. The two primary data sets, AVHRR NDVI time-series imagery and United States Department of Agriculture (USDA) regional crop yield data, are described. Details of the study regions, crop types, and time periods under investigation are specified. Three image masking procedures are discussed, two of which are evaluated in the research.
Finally, the modeling approach and performance evaluation framework are described, along with a summary of results and conclusions drawn from the analysis. Details of the modeling strategy used are described in the Appendix.
Related research
Traditionally, yield estimations are made through agrometeorological modeling or by compiling survey information provided throughout the growing season. Yield estimates derived from agro-meteorological models use soil properties and daily weather data as inputs to simulate various plant processes at a field level (Wiegand & Richardson, 1990; Wiegand et al., 1986). At this scale, agro-meteorological crop yield modeling provides useful results. However, at regional
scales these models are of limited practical use because of spatial differences in soil characteristics and crop growthdetermining factors such as nutrition levels, plant disease, herbicide and insecticide use, crop type, and crop variety, which would make informational and analytical costs excessive. Additionally, Rudorff and Batista (1991) indicated that, at a regional level, agro-meteorological models are unable to completely simulate the different crop growing conditions that result from differences in climate, local weather conditions, and land management practices. The scale of applicability of agrometeorological models is getting larger, though, but presently only through the integration of remotely sensed imagery. For instance, Doraiswamy et al. (2003) developed a method using AVHRR NDVI data as proxy inputs to an agro-meteorological model in estimating spring wheat yields at county and subcounty scales in the U.S. state of North Dakota.
In the past 25 years, many scientists have utilized remote sensing techniques to assess agricultural yield, production, and crop condition. Wiegand et al. (1979) and Tucker et al. (1980) first identified a relationship between the NDVI and crop yield using experimental fields and ground-based spectral radiometer measurements. Final grain yields were found to be highly correlated with accumulated NDVI (a summation of NDVI between two dates) around the time of maximum greenness
(Tucker et al., 1980). In another experimental study, Das et al. (1993) used remotely sensed data to predict wheat yield 85– 110 days before harvest in India. These early experiments identified relationships between NDVI and crop response, paving the way for crop yield estimation using satellite imagery.
Rasmussen (1992) used 34 AVHRR images of Burkina Faso, Africa, for a single growing season to estimate millet yield. Using accumulated NDVI and statistical regression techniques, he found strong correlations between accumulated NDVI and yield, but only during the reproductive stages of crop growth. The lack of a strong correlation between accumulated NDVI and yield during other stages of growth was attributed to the limited temporal profile of imagery used in the study and the high variability of millet yield in his study area. Potdar (1993) estimated sorghum yield in India using 14 AVHRR images from the same growing season. He was able to forecast actual yield at an accuracy of T15% up to 45 days before harvest. Rudorff and Batista (1991) used NDVI values
as inputs into an agro-meteorological model to explain nearly 70% of the variation in 1986 wheat yields in Brazil. Hayes and Decker (1996) used AVHRR NDVI data to explain more than 50% of the variation in corn yields in the United States Corn Belt. Each of these studies found positive relationships between crop yield and NDVI, but the strength of the relationships depended upon the amount and quality of the imagery used.
Some studies have used large, multi-year AVHRR NDVI data sets. Maselli et al. (1992) found strong correlations between NDVI and final crop yields in the Sahel region of Niger using 3 years of AVHRR imagery (60 images). In India, Gupta et al. (1993) used 3 years of AVHRR data to estimate wheat yields within T5% up to 75 days before harvest. The success of this study was dependent on the fact that over 80% of the study area was covered with wheat. In Greece, 2 years of AVHRR imagery were used to estimate crop yields (Quarmby et al., 1993). Actual harvested rice yields were predicted with an accuracy of T10%, and wheat yields were predicted with an accuracy of T12% at the time of maximum greenness. Groten (1993) was able to predict crop yield with a T15% estimation error 60 days before harvest in Burkina Faso using regression techniques and 5 years of AVHRR NDVI data (41 images).
Doraiswamy and Cook (1995) used 3 years of AVHRR NDVI imagery to assess spring wheat yields in North and South Dakota in the United States. They concluded that the most promising way to improve the use of AVHRR NDVI for estimating crop yields at regional scales would be to use larger temporal data sets, better crop masks, and climate data. Lee et al. (1999) used a 10-year, biweekly AVHRR data set to forecast  corn yields in the U.S. state of Iowa. They found that the most accurate forecasts of crop yield were made using a cropland mask and measurements of accumulated NDVI. Maselli and Rembold (2001) used multi-year series of annual crop yields and monthly NDVI to develop cropland masks for four Mediterranean African countries. They found that application of the derived cropland masks improved relationships between NDVI and final yield during optimal yield prediction periods. Ferencz et al. (2004) found yields of eight different crops in Hungary to be highly correlated with optimized, weighted seasonal NDVI sums using 1-km AVHRR NDVI from 1996 to 2000. They used non-forest vegetation masks and a novel time series interpolation approach and actually obtained their best results when using a greenness index equivalent to the numerator of the NDVI formula (NIR-RED; see Section 3). Additionally, many researchers have found that crop condition and yield estimation are improved through the inclusion of metrics that characterize crop development stage (Badhwar & Henderson, 1981; Groten, 1993; Kastens, 1998; Lee et al., 1999; Quarmby et al., 1993; Rasmussen, 1992). Ancillary data have been found useful as well. For example, Rasmussen (1997) found that soil type information improved the explanation of millet and ground nut yield variation using 3 years of AVHRR NDVI from the Peanut Basin in Senegal. In a later study, Rasmussen (1998) found that the inclusion of tropical livestock unit density further improved the explanation of millet yield variation in intensively cultivated regions of the Peanut Basin.

Based on the studies described, for the purpose of crop yield forecasting, longer time series of NDVI imagery are preferred to shorter ones. Also, few image masking techniques have been thoroughly and comparatively explored, likely due to the inherent complexities underlying this phase in any remote sensing-based yield forecasting methodology. Thus, to help achieve the goal of this project, an important objective of this research is to use historical yield information and historical time series AVHRR NDVI imagery to devise a thorough and robust statistical procedure for obtaining early to mid-season crop yield forecasts, with particular emphasis on image masking. The techniques described in this paper can be applied to any (region, crop)-pair that possesses sufficient historical yield information and corresponding time series NDVI imagery. Since few meaningful crop phenology metrics can be accurately derived at early points in the growing season, our research does not attempt to use this information. Also, no ancillary information is used, to prevent dependence on the availability of such data.

Description of data
The research presented in this paper relies on two data sets. The first is a time series of biweekly AVHRR NDVI compositeimagery from 1989 to 2000, obtained from the U.S. Geological survey Earth Resources Observation Systems (EROS) Data Center (EDC) in Sioux Falls, SD (Eidenshink, 1992). This data set was chosen because it is relatively inexpensive, reliable, and is updated in near real-time. NDVI is defined by the formula (NIR-RED)/(NIR +RED), where NIR is reflectance in the near-infrared spectrum (0.75 – 1.10 Am) and RED is reflectance in the red band of the visible spectrum (0.58 – 0.68 Am). Chlorophyll uses electromagnetic energy in the RED band for photosynthesis, and plant structure is reflective of energy in the NIR band. So, for vegetated surfaces, NDVI increases if plant biomass increases or if photosynthetic activity increases.
The NDVI data were received in unsigned 8-bit integer format, with the original NDVI range [1,1] linearly scaled to the integer range 0 –200. For analysis purposes, the integer data were rescaled to their native range of [1,1]. As a consequence of the limited precision of the original 8-bit data, the precision of the rescaled data is 0.01, so there is an implicit expected numerical error of 0.005 in the pixel-level NDVI values.
The NDVI data set is not without uncertainty, both temporal and spatial. From 1989 – 2000, two polar orbiting National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-11 [1989 – 1994] and NOAA-14 [1995 – 2000]) carried the AVHRR sensors that collected the data comprising our data set. The U.S. annually invariant target curve, with NDVI from periods 5– 21 (February 26 –October 21) shown for each year. This curve represents the average time series of nearly 3500 pixels selectively sampled from the 48 states in the conterminous U.S. to possess highly regular annual periodicity, thus exposing any artificial interannual NDVI value drift (Kastens et al., 2003). The NDVI data originating from NOAA-11 are fairly consistent over time. The data from NOAA-14 are less so, exhibiting a large artificial oscillation from 1997 to 2000. The range of the trend curve from 1989 to 2000 has width 0.0464. Comparing this width to
the overall effective range of the AVHRR NDVI data being used (which is approximately [0.05,0.95] for the full U.S. terrestrial range, but narrower in most practical situations), it follows that nearly 5% of the effective AVHRR NDVI data range can be attributed to artificial interannual NDVI value drift. In retrospect, we know that sensor orbit decay and sensor calibration degradation were the primary sources of the interannual NDVI value drift found in the NOAA-14 data.
Image resolution (1 km2 /pixel, or 100 ha/pixel) of the AVHRR NDVI data is also an issue because pixel size is more  than twice as large as the typical field size for soybeans and major grains in the U.S., which is roughly 40 ha (Kastens & Dhuyvetter, 2002). Furthermore, when considering spatial error of the image registration performed during the NDVI compositing process, the area of the region from which a single pixel’s values can be obtained grows to more than 4 km2, or more than 400 ha (Eidenshink, 1992). A combination of sensor factors (e.g., sensor stability, view angle, orbit integrity) and effects of image pre-processing and compositing induce this spatial variation.

The second data set is historical, final, state-level yield data, obtained from the USDA National Agricultural Statistics Service (NASS) through its publicly accessible website (http://www.usda.gov/nass). The database is updated annually for all crops, with each particular crop’s final regional yield estimates released well after harvest completion. Updates to the final regional yield estimates can occur up to 3 years after their initial release, but generally these changes are not large. No historical or expected error statistics for these estimates are published below the national spatial scale, but they are nonetheless accepted by the industry as the best widely available record for average regional crop yield in the U.S.

Description of crops, regions, and time periods under investigation
The crops and regions under investigation in the present research are corn and soybeans in the U.S. states of Iowa (IA) and Illinois (IL), winter wheat and grain sorghum in the state of Kansas (KS), and spring wheat and barley in the state of North Dakota (ND). Compared to other states, during 1989 –2000, Iowa ranked first in corn production (100.2 million mt/year; ‘‘mt’’=metric ton) and second in soybean production (26.9 million mt/year). Illinois ranked second in corn production (89.9 million mt/year) and first in soybean production (27.1 million mt/year). Kansas was the top-producing winter wheat state (25.5 million mt/year) and the top-producing grain sorghum state (14.2 million mt/year). North Dakota was the top-producer of both spring wheat (16.6 million mt/year) and barley (6.3 million mt/year).

For each crop, a six-period window of early to mid-season NDVI imagery is considered. The source data for these six biweekly composites span nearly 3 months of raw AVHRR imagery, corresponding to Julian biweekly periods 5 – 10 (approximately February 26 –May 20) for winter wheat, 9– 14 (approximately April 23 – July 15) for spring wheat and barley, and 11 –16 (approximately May 21–August 12) for corn, soybeans, and sorghum. Labeling the six biweekly periods 1 to 6, yields are modeled using data from periods 1– 4, 1 –5, and 1 – 6, with each of these three ranges providing a unique yield forecasting opportunity corresponding to a
different point in the growing season. To obtain the dates for the crop-specific ranges, the initial release dates of USDA NASS yield forecasts were considered. The first NDVI image generated after the release of the initial USDA state-level estimates for the season is assigned to period 6, which fixes periods 1– 5 as well. Initial release dates for USDA state level estimates are approximately May 11 for winter wheat, July 11 for spring wheat and barley, and August 11 for corn, soybeans, and grain sorghum. Hereafter, winter wheat will be classified as an early-season crop, spring wheat and barley as mid-season crops, and corn, soybeans, and grain sorghum as late-season crops. With this timing, forecasts generated at periods 4 and 5 for each crop are produced before any state-level USDA yield estimates are released.

Approaches to image masking in crop yield forecasting
The purpose of image masking in the context of crop yield forecasting is to identify subsets of a region’s pixels that lead to NDVI variable values that are optimal indicators of a particular crop’s final yield. A good image mask should capture the essence (i.e., salient features) of the present year’s growing season with respect to how the crop of interest is progressing. This growing season essence is a combination of climatic and terrestrial factors.
Crop-specific masking
In theory, the ideal approach to image masking for the purpose of crop yield forecasting would be to use crop-specific masking. This would allow one to consider only NDVI information pertaining to the crop of interest. However, when such masking is applied to multiple years of imagery, several difficulties are encountered. Principal among these is the widespread practice of crop rotation, which suggests that year-specific masks are needed rather than a single cropspecific mask that can be applied to all years. Regional trending in crop area (increase or decrease in the amount of a region’s area planted to a particular crop over time), if severe
enough, also may call for year-specific masking. Identifying a particular crop in the year to be forecasted presents even greater difficulties, as only incomplete growing season NDVI information is available. This is especially true early in the season when the crop has low biomass and does not produce a large
NDVI response. In addition to hindering crop classification, this low NDVI response of a crop early in its development also  stifles crop yield modeling efforts, as AVHRR NDVI measurements from pixels corresponding to immature crops are not very sensitive and are thus minimally informative (see Wardlow et al., in press, for an example of such insensitivity occurring with 250-m Moderate Resolution Imaging Spectroradiometer [MODIS] NDVI data, and MODIS has better radiometric resolution than AVHRR). Moreover, with the coarse-resolution (about 100 hectares/ pixel) AVHRR NDVI imagery used in this study, identifying monocropped pixels becomes an improbable task. This is particularly true in low-producing regions and in regions with sparse crop distribution. As noted, a single pixel covers an area well over twice the average field size, and when error of the image registration is considered, a pixel’s effective ground coverage can become more than 400 hectares/pixel, or roughly ten times the typical field size.

Cropland masking
A more feasible alternative to crop-specific masking is cropland masking, which refers to using pixels dominated by land in general agricultural crop production. Kastens (1998, 2000) and Lee et al. (1999) obtained some of their best yield modeling results using this approach. Rasmussen (1998) used a percent-cropland map to improve his yield modeling by splitting the data into two categories based on cropland density and building different models for the two classes. Maselli and Rembold (2001) used correlations between 13 years of monthly NDVI composites and 13- year series of national crop yields to estimate pixel-level cropland fraction for four Mediterranean African countries. Upon application of these derived cropland masks, the authors found improved relationships between NDVI and
final estimated yield.
Cropland masks usually are derived from existing land use/land cover maps (one exception being Maselli and Rembold (2001)). If relatively small amounts of land in a study area have been taken out of or put into agricultural crop production during a study period, a single mask can be obtained and applied to all years of data. Considering that all traditional agricultural crops are now lumped in the general class of ‘‘cropland,’’ heavily cropped pixels are more prevalent in heavily cropped regions, which allows for the construction of well-populated masks dominated by cropland. But the generation of such masks becomes difficult when low-producing regions are encountered, as well as in regions where cropland is widely interspersed with non-cropland.
As with crop-specific masking, cropland masking also can suffer the effects of minimally informative NDVI response early in a crop’s growing season (e.g., March for early-season crops, May for mid-season crops, and late May and early June for late-season crops). Many important agricultural regions are almost completely dominated by single-season crop types (e.g., Iowa produces predominately late-season crops). In such cases, cropland AVHRR NDVI from time periods early in the
particular growing season may not be very useful for predicting final yield.

By late May and June, some of the year’s terrestrial and weather-based growth-limiting factors may have already been established for some crops or regions. For instance, in the U.S., soil moisture has largely been set by this time, and soil moisture is an important determinant of crop yields in the four states comprising our study area. Such moisture information is not readily detectable in immature crops because it is usually not a limiting factor until the plant’s water needs become
significant and its roots penetrate deeper into the soil. On the other hand, available soil moisture can noticeably affect other regional vegetation that is already well developed, such as grasslands, shrublands, and wooded areas, and in some cases, early- and mid-season crops.

Yield-correlation masking
For the reasons noted above, we propose a new masking technique, which we call yield-correlation masking. All vegetation in a region integrates the season’s cumulative growing conditions in some fashion and may be more indicative of a crop’s potential than the crop itself. Thus, all pixels are considered for use in crop yield prediction. This premise is most sound early in a crop growing season (especially for mid- and late-season crops), when the NDVI response of the immature crop is not yet strong enough to be a useful indicator of final yield. Also, as noted, when the crop is in early growth stages, problems such as lack of subsoil moisture may not yet have impacted the immature crop while having already affected more mature nearby vegetation.
Each NDVI-based variable captures a different aspect of the current growing season. This aspect manifests itself in different ways within the region’s vegetation, suggesting that optimal masks for the different NDVI-based variables likely will not be identical. Thus, for each (region, crop)-pair, yield-correlation masking generates a unique mask for each NDVI variable. The technique is initiated by correlating each of the historical, pixel-level NDVI variable values with the
region’s final yield history, a strategy similar to the initial step of the cropland classification strategy presented in Maselli and Rembold (2001). The highest correlating pixels,  thresholded so that some pre-specified number of pixels is included in the mask (this issue is addressed later), are retained for further processing and evaluation of the variable at hand.
Though much more computationally intensive, the yieldcorrelation masking technique overcomes the major problems afflicting crop-specific masking and cropland masking. Unlike these approaches, yield-correlation masking readily can be applied to low-producing regions and regions possessing sparse crop distribution. Also, since yield correlation masks are not constrained to include pixels dominated by cropland, they are not necessarily hindered by the weak and insensitive NDVI
responses exhibited by crops early in their respective growing seasons. Furthermore, once the issue of identifying optimal mask size (i.e., determining how many pixels should be  included in the masks) is addressed, the entire masking/ modeling procedure becomes completely objective.

Description of cropland masks
For Iowa, Illinois, and North Dakota, the cropland masks used in this study were derived from the United States Geological Survey (USGS) National Land Cover Database (NLCD) (Vogelmann et al., 2001). The original 30-m resolution land cover maps can be obtained from the website http://landcover.usgs.gov/natllandcover.html. After generalizing the classes to cropland and non-cropland, the data were aggregated to a 1-km grid corresponding to the NDVI imagery used in this study. All annual crops, as well as alfalfa, were assigned to the cropland category, and all other cover types
were classified as non-cropland. Pixel values in the resulting  map corresponded to percent cropland within the 1-km2 footprint of the pixel.

(Source – http://kufs.ku.edu/media/uploads/work/Kastens_RSE2005_Image_masking_for_crop_yield_forecasting.pdf)

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Agricultural Meteorology

            A branch of meteorology that examines the effects and impacts of weather and climate on crops, rangeland, livestock, and various agricultural operations. The branch of agricultural meteorology dealing with atmospheric-biospheric processes occurring at small spatial scales and over relatively short time periods is known as micrometeorology, sometimes called crop micrometeorology for managed vegetative ecosystems and animal biometeorology for livestock operations. The branch that studies the processes and impacts of climatic factors over larger time and spatial scales is often referred to as agricultural climatology.

            Agricultural meteorology, or agrometeorology, addresses topics that often require an understanding of biological, physical, and social sciences. It studies processes that occur from the soil depths where the deepest plant roots grow to the atmospheric levels where seeds, spores, pollen, and insects may be found. Agricultural meteorologists characteristically interact with scientists from many disciplines.

          Agricultural meteorologists collect and interpret weather and climate data needed to understand the interactions between vegetation and animals and their atmospheric environments.

            The climatic information developed by agricultural meteorologists is valuable in making proper decisions for managing resources consumed by agriculture, for optimizing agricultural production, and for adopting farming practices to minimize any adverse effects of agriculture on the environment. Such information is vital to ensure the economic and environmental sustainability of agriculture now and in the future. Agricultural meteorologists also quantify, evaluate, and provide information on the impact and consequences of climate variability and change on agriculture. Increasingly, agricultural meteorologists assist policy makers in developing strategies to deal with climatic events such as floods, hail, or droughts and climatic changes such as global warming and climate variability.

           Agricultural meteorologists are involved in many aspects of agriculture, ranging from the production of agronomic and horticultural crops, trees, and livestock to the final delivery of agricultural products to market. They study the energy and mass exchange processes of heat, carbon dioxide, water vapor, and trace gases such as methane, nitrous oxide, and ammonia, within the biosphere on spatial scales ranging from a leaf to a watershed and even to a continent.

            They study, for example, the photosynthesis, productivity, and water use of individual leaves, whole plants, and fields. They also examine climatic processes at time scales ranging from less than a second to more than a decade.

            Agricultural Climatology

            In general, the study of climate as to its effect on crops; it includes, for example, the relation of growth rate and crop yields to the various climatic factors and hence the optimum and limiting climates for any given crop. Also known as agroclimatology.

            Biometeorology

            A branch of meteorology and ecology that deals with the effects of weather and climate on plants, animals, and humans. The principal problem for living organisms is maintaining an acceptable thermal equilibrium with their environment. Organisms have natural techniques for adapting to adverse conditions. These techniques include acclimatization, dormancy, and hibernation, or in some cases an organism can move to a more favorable environment or microenvironment. Humans often establish a favorable environment through the use of technology.

            Climatology

            The scientific study of climate. Climate is the expected mean and variability of the weather conditions for a particular location, season, and time of day. The climate is often described in terms of the mean values of meteorological variables such as temperature, precipitation, wind, humidity, and cloud cover. A complete description also includes the variability of these quantities, and their extreme values. The climate of a region often has regular seasonal and diurnal variations, with the climate for January being very different from that for July at most locations. Climate also exhibits significant year-to-year variability and longer-term changes on both a regional and global basis. The goals of climatology are to provide a comprehensive description of the Earth’s climate over the range of geographic scales, to understand its features in terms of fundamental physical principles, and to develop models of the Earth’s climate for sensitivity studies and for the prediction of future changes that may result from natural and human causes.

            Crop Micrometeorology

            The branch of meteorology that deals with the interaction of crops and their immediate physical environment.

            Micrometeorology

            The study of small-scale meteorological processes associated with the interaction of the atmosphere and the Earth’s surface. The lower boundary condition for the atmosphere and the upper boundary condition for the underlying soil or water are determined by interactions occurring in the lowest atmospheric layers. Momentum, heat, water vapor, various gases, and particulate matter are transported vertically by turbulence in the atmospheric boundary layer and thus establish the environment of plants and animals at the surface. These exchanges are important in supplying energy and water vapor to the atmosphere, which ultimately determine large-scale weather and climate patterns. Micrometeorology also includes the study of how air pollutants are diffused and transported within the boundary layer and the deposition of pollutants at the surface.

            In many situations, atmospheric motions having time scales between 15 min and 1 h are quite weak. This represents a spectral gap that provides justification for distinguishing micrometeorology from other areas of meteorology. Micrometeorology studies phenomena with time scales shorter than the spectral gap (time scales less than 15 min to 1 h and horizontal length scales less than 2–10 km). Some phenomena studied by micrometeorology are dust devils, mirages, dew and frost formation, evaporation, and cloud streets.

            Ecosystem

            An ecosystem is a complete community of living organisms and the nonliving materials of their surroundings. Thus, its components include plants, animals, and microorganisms; soil, rocks, and minerals; as well as surrounding water sources and the local atmosphere. The size of ecosystems varies tremendously. An ecosystem could be an entire rain forest, covering a geographical area larger than many nations, or it could be a puddle or a backyard garden. Even the body of an animal could be considered an ecosystem, since it is home to numerous microorganisms. On a much larger scale, the history of various human societies provides an instructive illustration as to the ways that ecosystems have influenced civilizations.

            Weather Observations

            The measuring, recording, and transmitting of data of the variable elements of weather. In the India the National Data Centre (NDC), a division of the India Meteorological Department (IMD), has as one of its primary responsibilities the acquisition of meteorological information.

            The data are sent by various communication methods to the NDC of IMD. At the Center, the raw data are fed into large computers that are programmed to plot, analyze, and process the data and also to make prognostic weather charts. The processed data and the forecast guidance are then distributed by special National Weather Service systems and conventional telecommunications to field offices, other government agencies, and other stake holders. They in turn prepare forecasts and warnings based on both processed and raw data. A wide variety of meteorological data are required to satisfy the needs of meteorologists, climatologists, and users in marine activities, forestry, agriculture, aviation, and other fields.

            Weather forecasting and prediction

            Processes for formulating and disseminating information about future weather conditions based upon the collection and analysis of meteorological observations. Weather forecasts may be classified according to the space and time scale of the predicted phenomena. Atmospheric fluctuations with a length of less than 100 m (330 ft) and a period of less than 100 s are considered to be turbulent.

            The study of atmospheric turbulence is called micrometeorology; it is of importance for understanding the diffusion of air pollutants and other aspects of the climate near the ground. Standard meteorological observations are made with sampling techniques that filter out the influence of turbulence. Common terminology distinguishes among three classes of phenomena with a scale that is larger than the turbulent microscale: the mesoscale, synoptic scale, and planetary scale.

            The mesoscale includes all moist convection phenomena, ranging from individual cloud cells up to the convective cloud complexes associated with prefrontal squall lines, tropical storms, and the intertropical convergence zone. Also included among mesoscale phenomena are the sea breeze, mountain valley circulations, and the detailed structure of frontal inversions. Most mesoscale phenomena have time periods less than 12 h. The prediction of mesoscale phenomena is an area of active research. Most forecasting methods depend upon empirical rules or the short-range extrapolation of current observations, particularly those provided by radar and geostationary satellites.

            Forecasts are usually couched in probabilistic terms to reflect the sporadic character of the phenomena.

            Since many mesoscale phenomena pose serious threats to life and property, it is the practice to issue advisories of potential occurrence significantly in advance. These “watch” advisories encourage the public to attain a degree of readiness appropriate to the potential hazard. Once the phenomenon is considered to be imminent, the advisory is changed to a “warning,” with the expectation that the public will take immediate action to prevent the loss of life.

            The next-largest scale of weather events is called the synoptic scale, because the network of meteorological stations making simultaneous, or synoptic, observations serves to define the phenomena. The migratory storm systems of the extratropics are synoptic-scale events, as are the undulating wind currents of the upper-air circulation which accompany the storms. The storms are associated with barometric minima, variously called lows, depressions, or cyclones. The synoptic method of forecasting consists of the simultaneous collection of weather observations, and the plotting and analysis of these data on geographical maps. An experienced analyst, having studied several of these maps in chronological succession, can follow the movement and intensification of weather systems and forecast their positions. This forecasting technique requires the regular and frequent use of large networks of data.

            Planetary-scale phenomena are persistent, quasistationary perturbations of the global circulation of the air with horizontal dimensions comparable to the radius of the Earth. These dominant features of the general circulation appear to be correlated with the major orographic features of the globe and with the latent and sensible heat sources provided by the oceans. They tend to control the paths followed by the synoptic-scale storms, and to draw upon the synoptic transients for an additional source of heat and momentum.

            Numerical weather prediction is the prediction of weather phenomena by the numerical solution of the equations governing the motion and changes of condition of the atmosphere. Numerical weather prediction techniques, in addition to being applied to short-range weather prediction, are used in such research studies as air-pollutant transport and the effects of greenhouse gases on global climate change.

            The first operational numerical weather prediction model consisted of only one layer, and therefore it could model only the temporal variation of the mean vertical structure of the atmosphere.

            Computers now permit the development of multilevel (usually about 10–20) models that could resolve the vertical variation of the wind, temperature, and moisture. These multilevel models predict the fundamental meteorological variables for large scales of motion. Global models with horizontal resolutions as fine as 125 mi (200 km) are being used by weather services in several countries. Global numerical weather prediction models require the most powerful computers to complete a 10-day forecast in a reasonable amount of time.

            Research models similar to global models could be applied for climate studies by running for much longer time periods. The extension of numerical predictions to long time intervals (many years) requires a more accurate numerical representation of the energy transfer and turbulent dissipative processes within the atmosphere and at the air-earth boundary, as well as greatly augmented computing-machine speeds and capacities.

            Long-term simulations of climate models have yielded simulations of mean circulations that strongly resemble those of the atmosphere. These simulations have been useful in explaining the principal features of the Earth’s climate, even though it is impossible to predict the daily fluctuations of weather for extended periods. Climate models have also been used successfully to explain paleoclimatic variations, and are being applied to predict future changes in the climate induced by changes in the atmospheric composition or characteristics of the Earth’s surface due to human activities.

            Surface meteorological observations are routinely collected from a vast continental data network, with the majority of these observations obtained from the middle latitudes of both hemispheres. Commercial ships of opportunity, military vessels, and moored and drifting buoys provide similar in-place measurements from oceanic regions. Information on winds, pressure, temperature, and moisture throughout the troposphere and into the stratosphere is routinely collected from (1) balloon-borne instrumentation packages (radiosonde observations) and commercial and military aircraft which sample the free atmosphere directly; (2) ground-based remote-sensing instrumentation such as wind profilers (vertically pointing Doppler radars), the National Weather Service Doppler radar network, and lidars; and (3) special sensors deployed on board polar orbiting or geostationary satellites. The remotely sensed observations obtained from meteorological satellites have been especially helpful in providing crucial measurements of areally and vertically averaged temperature, moisture, and winds in data-sparse (mostly oceanic) regions of the world. Such measurements are necessary to accommodate modern numerical weather prediction practices and to enable forecasters to continuously monitor global storm (such as hurricane) activity.

            Forecast products and forecast skill are classified as longer term (greater than 2 weeks) and shorter term. These varying skill levels reflect the fact that existing numerical prediction models such as the medium-range forecast have become very good at making large-scale circulation and temperature forecasts, but are less successful in making weather forecasts. An example is the prediction of precipitation amount and type given the occurrence of precipitation and convection.

            Each of these forecasts is progressively more difficult because of the increasing importance of mesoscale processes to the overall skill of the forecast.

            Nowcasting is a form of very short range weather forecasting. The term nowcasting is sometimes used loosely to refer to any area-specific forecast for the period up to 12 h ahead that is based on very detailed observational data. However, nowcasting should probably be defined more restrictively as the detailed description of the current weather along with forecasts obtained by extrapolation up to about 2 h ahead. Useful extrapolation forecasts can be obtained for longer periods in many situations, but in some weather situations the accuracy of extrapolation forecasts diminishes quickly with time as a result of the development or decay of the weather systems.

            Forecasts of time averages of atmospheric variables, for example, sea surface temperature, where the lead time for the prediction is more than 2 weeks, are termed long-range or extended-range climate predictions. Extended-range predictions of monthly and seasonal average temperature and precipitation are known as climate outlooks. The accuracy of long-range outlooks has always been modest because the predictions must encompass a large number of possible outcomes, while the observed single event against which the outlook is verified includes the noise created by the specific synoptic disturbances that actually occur and that are unpredictable on monthly and seasonal time scales. According to some estimates of potential predictability, the noise is generally larger than the signal in middle latitudes.

(Source – http://hau.ernet.in/coa/Kagromet.pdf)

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DOES NO-TILL FARMING REQUIRE MORE HERBICIDES?

“More” compared with…?

There is obviously a wide variety of no-till farming systems and so there is an equally wide variety of conventional tillage based agricultural systems. The use of herbicides is a common feature and widespread practice in many intensive farming systems. This applies equally to tillage based conventional farming as to no-till farming. Herbicides are a useful tool for weed management, particularly in the first years after shifting from conventional farming to no-till farming. It is much easier, to do no-till farming with herbicides than without.  If now no-till farming is introduced in an environment of traditional peasant farming, where no herbicides are used at all, these no-till farming systems will obviously use “more” herbicides than the traditional conventional systems.

However, in many conventional systems herbicides are already frequently used and mechanical weed control has nearly disappeared in intensive farming. In such a system, the shift to no-till farming might not necessarily increase the use of herbicides dramatically. Even where it does increase the amount of active ingredient applied per area and year, the environmental impact is not necessarily worse, as often there is a shift from herbicides with relatively high environmental impact to other herbicides with less impact.

Therefore, it is difficult to generalize and no-till farming systems might not always require more herbicides than conventional farming systems.

What are the conditions for increased herbicide use under no-till?

Nevertheless, most of the scientific literature shows that notill farming does in fact require more herbicides than conventional systems comparing similar cropping systems.

There is no doubt that there are significant areas under notillage systems, where herbicide overuse is creating environmental problems. These systems are characterized by monocultures and, in absence of soil tillage, by herbicide use being the only weed management strategy applied. These areas are the ultimate proof for the statement, that no-till farming uses more herbicides. Many of these areas are also cropped with genetically modified crops, which are resistant to a specific herbicide. Therefore, the herbicide use in these cases is restricted to a single product. However, under such a condition, even soil tillage would not really improve the herbicide use. Such cropping systems, with or without tillage, can be considered as not conforming to good agricultural practice.

What is the weed control effect  of tillage?

Soil tillage has been developed for a number of reasons, such as to facilitate the preparation of a seedbed for a more efficient seeding. However, weed control has always been attributed to soil tillage and, particularly, the development of the mouldboard plough was very effective for weed management. But, in the long term, the weed control effect of tillage has proven to be insufficient and herbicides have become the tool of choice in intensive farming. The problem of tillage is that by creating a good seedbed for the seeds, it creates the same conditions for the weeds. While weed seeds are buried deeply with the mouldboard plough, the same plough brings to the surface the weed seeds that had been buried the season before. The seed bank in most agricultural soils is probably large enough that the plough does not have a long lasting control effect on weeds which multiply by seeds. On the other side, weeds propagating through sprouts or roots can even be multiplied by tillage implements, which only cut and mix them with the soil, so that the number of potential weed plants is increased. Through soil carried with tillage implements from one field to another, the weed population is also spread throughout the entire farmland.

Therefore, the use of tillage for weed control is not the ultimate answer, nor is the move to no-till the ultimate doom in terms of weed control.

How can herbicide use be  reduced?

This brings us back to herbicides. In all farming situations, not only in no-till farming, the use of herbicides can be reduced by applying the products correctly, using the right equipment with the appropriate settings under optimal conditions. Often the application of herbicide is done with even less care than the application of other pesticides, as herbicides are usually considered less toxic than, for example, insecticides. It leads then to increased application rates as the product is not reaching the target, but is wasted in the environment. This can become a problem, where herbicides have not been used traditionally and where, therefore, there is no appropriate equipment available for the application of herbicides once more intensive farming systems are introduced. For example, in the case of Uzbekistan, farmers start using the existing air blast sprayers, which are traditionally used for application of defoliants in cotton, for herbicide application. Similar cases can be found in other

Central Asian countries, such as Mongolia or Kazakhstan, where frequent cultivation of black fallow has been the only weed management strategy for the past few years and where the spray rigs are sometimes in very bad conditions. In FAO projects carried out in these countries, the simple upgrade of existing sprayers with upgrade kits, comprising pumps, controls, hoses and nozzles, reduced the herbicide use compared to farmers practice before the upgrade by 10 to 15 % while the weed control efficiency was at the same time improved by 20 % to values above 90 % control.

What are alternatives for weed management under no-till?

However, the main question remains, whether there are any alternative strategies for weed control that are applicable in no-till farming systems and which would allow reducing the dependency on herbicides. There is actually a wide range of options and principles within a weed management strategy that allow managing weeds without tillage and herbicides.

This starts with a forward looking strategy of weed control, to avoid the maturation and seeding of weeds in the first place by not allowing weed growth even in the off season. Applying this strategy, the farmers in an FAO project in Kazakhstan noticed after only two years of no-till cropping without even using a diversified crop rotation that the weed pressure and, hence, the need for herbicide use was being reduced compared to the conventional tillage based systems.

Another general point is to determine, at which point weeds are actually damaging the crop. It is often not necessary to eradicate the weeds completely, but only to avoid the setting of seeds and competition with the crop. Leaving weeds in a crop at a stage where the crop can suppress them and where there is no damage or problem for the harvest can actually help with managing other pests, such as termites or ants, which in absence of weeds would damage the crop.

A second aspect comes from the soil tillage itself. Farmers who do no-till for several years will notice that weed germination is reduced where the soil is not touched. Once the superficial weed-seed bank is depleted and no new seeds are added, the other seeds still remaining in the soil will not germinate as they will not receive the light stimulus for germination. For this reason, the no-till planters from Brazil,

for example, where no-till farming is reaching nearly 50 % of the total agricultural area, are designed to avoid any soil movement and to cover the seed slot immediately with mulch to create an “invisible” no-till seeding. This is done to reduce the emergence of weed seeds

The most powerful no-tillage and non-chemical weed control in no-till systems, however, is soil cover and crop rotation. Maintaining the soil covered with an organic mulch or a live crop can allow, under certain conditions, notill farming without using any herbicide. For this purpose, it is important to know the allelopathic effects of cover crops. These effects result from substances in the plants which can suppress other plant growth. Cover crops are crops which can be grown between commercial crops to maintain permanent soil cover. Crop rotations have to be designed in such a way, that the soil is always covered and that the variety of crops in the rotation facilitates the management of weeds. For managing the cover crops, a knife roller is used, which breaks the plants and rolls them down.

Applied at the right time, this tool can actually kill some of the cover crops without need of herbicide and achieve complete weed control throughout the next cropping season, provided the planting is done with minimum soil movement. Applying a knife roller, for example, in a well developed cover crop of black oat (Avena strigosa) at milk stage, will completely kill the cover crop, which on the other side will provide good weed control. In Brazil after a cover crop of black oat, there is usually no additional herbicide applied for the following crop There is a lot of scientific and practical evidence that weed infestation under no-till farming using certain cover crops and diversified crop rotations is declining in the long term, allowing a similar decline in herbicide use. Farmers using these principles of good agricultural practice in no-tillage systems report declining pesticide use in general, which also includes declining herbicide use at a level lower than comparable conventional systems.

Starting no-till farming with the establishment of good cover crops and a forward looking weed management allows the introduction of no-till farming in small holder farms in Africa without any herbicide use at all and with a reduction of manual weeding requirements. Spectacular effects were achieved in an FAO project in Swaziland using no chemical inputs and increasing both yields and reducing the drudgery of farm work by introducing a no-till farming system combined with permanent soil cover and crop rotation better known as Conservation Agriculture.

Conclusions

There is no question that herbicide use in agriculture and particularly in no-till farming systems can be a problem. There is plenty of scientific and practical evidence of excessive herbicide use in no-till farming. However, this is not an inherent characteristic of no-tillage farming, as there are alternative ways for weed management even without returning to soil tillage and cultivation. If correctly applied, these practices allow a sustainable use of herbicides in an integrated weed management programme  and even completely non-chemical weed control is possible. These practices are already successfully applied in commercial farming, but globally they are not yet sufficiently known or appreciated. Therefore, the general perception remains that no-tillage farming requires increased herbicide rates, which in reality not true as a general statement.

(Source – http://www.fao.org/ag/ca/CA-Publications/Pesticide%20Outlook%202005.pdf)

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Potatoes and Climate Change

As the fourth most important food crop after rice, wheat and maize, potatoes are of invaluable importance for the diets and livelihoods of millions of people worldwide.

The potato embarked on its successful journey around the globe in the 16th century, when the Spanish brought it to Europe from the South American Andes. From here, the potato found its way to Asia in the 17th century and to Africa in the 19th century. The crop’s comparably short vegetation period allows farmers throughout a wide range of different climatic conditions to find an appropriate season for its cultivation.

Global potato production has grown markedly in the past years, particularly due to increased production in developing countries. Improvements in crop varieties, seed potato and cultivation methods have led to higher yields. Moreover, a shift in eating habits in many countries towards more industrially processed potato-based products has boosted demand. In 2005, for the first time, more potatoes were grown in developing countries than in industrialised nations. The main producer is China, with a crop yield of 71 million tonnes, which amounts to over 20% of global production.

Global potato production has grown markedly in the past years, particularly due to increased production in developing countries. Improvements in crop varieties, seed potato and cultivation methods have led to higher yields. Moreover, a shift in eating habits in many countries towards more industrially processed potato-based products has boosted demand. In 2005, for the first time, more potatoes were grown in developing countries than in industrialised nations. The main producer is China, with a crop yield of 71 million tonnes, which amounts to over 20% of global production.

Potatoes are an important source of income for many farmers. In the Andes they are often the only cash crop grown by small farmers. In the tropical lowlands of Bangladesh and India they are cultivated mainly as an irrigated winter cash crop.

Potatoes enjoy particular popularity among farmers in the highlands of Vietnam, who profit from favourable prices. They grow the tubers as a catch crop, in rotation with rice and maize, and while the income they earn from potatoes equals that from rice, it amounts to twice what they could generate from maize and sweet potatoes.

Along with the familiar difficulties related to pests and diseases, potato farmers are increasingly confronted with abiotic problems. Farmers and researchers report an increase in water stress, changes in rainfall distribution and intensity, hail, and increasingly frequent frost and snowfall at high altitudes. The growing frequency of extreme weather events is generally interpreted as clearly related to climate change. The newest report by the Intergovernmental Panel of Climate Change (IPCC), published in 2007, states that global climate warming is an unequivocal fact.

Projections by the IPCC predict a rise in global temperature by 1.8–4°C by the year 2100 due to the increase in greenhouse gases, depending on the scenario. This is expected to have grave consequences for mankind and the environment. The critical threshold is said to be around a temperature increase of 2°C.

Approximately 15% of the total worldwide greenhouse gas emissions are caused by agriculture. An additional 11% result from deforestation, mainly for the purpose of gaining cropland.

Carbon dioxide (CO2) emissions in agriculture are chiefly caused by the use of fossil fuels during all kinds of agricultural activities, as well as tillage, burning of crop residues, and slash-and-burn deforestation. In addition, agriculture produces around half of the global methane (CH4) and nitrous oxide (N2O) emissions. These two greenhouse gases are many times more potent than carbon dioxide.

The main sources of CH4 are livestock production, irrigated rice cultivation, and storage of manure. N2O is released into the atmosphere through the soil following the inadequate application of artificial fertilisers and manure. By taking appropriate measures, agriculture has the possibility of reducing greenhouse gas emissions and thereby actively contributing to the mitigation of climate change… <more

(Source: InfoResources Focus)

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Potential Long-Term Benefits of No-Tillage and Organic Cropping Systems

There have been few comparisons of the performance of no-tillage cropping systems vs. organic farming systems, particularly on erodible, droughty soils where reduced-tillage systems are recommended. In particular, there is skepticism whether organic farming can improve soils as well as conventional no-tillage systems because of the requirement for tillage associated with many organic farming operations. A 9-yr comparison of selected minimum-tillage strategies for grain production of corn (Zea mays L.), soybean [Glycine max (L.) Merr.], and wheat (Triticum aestivum L.) was conducted on a sloping, droughty site in Beltsville, MD, from 1994 to 2002. Four systems were compared: (i) a standard mid-Atlantic no-tillage system (NT) with recommended herbicide and N inputs, (ii) a cover cropbased no-tillage system (CC) including hairy vetch (Vicia villosa Roth) before corn, and rye (Secale cereale L.) before soybean, with reduced herbicide and N inputs, (iii) a no-tillage crownvetch (Coronilla varia L.) living mulch system (CV) with recommended herbicide and N inputs, and (iv) a chisel-plow based organic system (OR) with cover crops and manure for nutrients and postplanting cultivation for weed control. After 9 yr, competition with corn by weeds in OR and by the crownvetch living mulch in CV was unacceptable, particularly in dry years. On average, corn yields were 28 and 12% lower in OR and CV, respectively, than in the standard NT, whereas corn yields in CC and NT were similar. Despite the use of tillage, soil combustible C and N concentrations were higher at all depth intervals to 30 cm in OR compared with that in all other systems. A uniformity trial was conducted from 2003 to 2005 with corn grown according to the NT system on all plots. Yield of corn grown on plots with a 9-yr history of OR and CV were 18 and 19% higher, respectively, than those with a history of NT whereas there was no difference between corn yield of plots with a history of NT and CC.

Three tests of N availability (corn yield loss in subplots with no N applied in 2003–2005, presidedress soil nitrate test, and corn ear leaf N) all confirmed that there was more N available to corn in OR and CV than in NT. These results suggest that OR can provide greater long-term soil benefits than conventional NT, despite the use of tillage in OR. However, these benefits may not be realized because of difficulty controlling weeds in OR.

No-tillage cropping systems have been shown to offer many benefits to soils and production of grain crops in the eastern USA (Grandy et al., 2006). After 28 yr of continuous tillage treatments in Ohio, the notillage system had higher organic C, cation-exchange capacity, hydraulic conductivity, aggregate diameter, and water-holding capacity than tillage systems (Mahboubi et al., 1993). On well-drained soils, corn and soybean yields were consistently higher with continuous no-tillage than conventional tillage (Dick et al., 1991). No-tillage systems were shown to reduce drought stress and increase yields of grain crops on upland soils in the piedmont of the southern states (Denton and Wagger, 1992). Corn root length density was higher in the top 0.1 m of soil under no-tillage than under conventional tillage, probably a result of higher water-holding capacity, capillary space, and proportion of water-stable aggregates in the surface soil (Ball-Coelho et al., 1998).

Many of the improvements to soils as a result of notillage production are related to increases in soil organic C which in turn relates to improvements in soil aggregation, water-holding capacity, and nutrient cycling (Weil and Magdoff, 2004; Grandy et al., 2006). Soil organic C can also be increased by other strategies, including addition of winter annual cover crops into rotations, diversifying rotations with perennial crops, addition of manure-based amendments, and organic farming, which often employs all of the preceding strategies. For example, soil organic C and N were increased by both reducing tillage and using winter annual cover crops, leading the authors to suggest that the best management system would include no-tillage and a mixture of legume and nonlegume winter annual cover crops (Sainju et al., 2002). Rotations that included at least 3 yr of perennial forage crops had the highest soil quality scores with total organic C being identified as the most sensitive quality indicator (Karlen et al., 2006). Manure- and legumebased organic farming systems from nine long-term experiments across the USA were shown to increase soil organic C and total N compared with conventional systems (Marriott and Wander, 2006). Crop yields and/or soil organic C was increased by organic vs. conventional cropping systems in the East (Pimentel et al., 2005), Midwest (Delate and Cambardella, 2004), and West (Clark et al., 1998).

Most comparisons of soil improvements in organic vs. conventional cropping systems have been conducted under conventional tillage conditions. The dilemma for organic farmers is that the approaches for increasing soil organic C usually require tillage. Specifically, tillage is required for eliminating perennial legumes before rotation to annual crops, for incorporating manure to avoid N volatilization losses, or for preparing a seedbed and controlling weeds. Since an increase in tillage intensity and frequency has been shown to decrease soil C and N (Franzluebbers et al., 1999; Grandy et al., 2006), increases in organic matter by utilization of organic materials in organic farming may be offset by decreases in organic matter from tillage. Some authors have speculated that conventional no-tillage agriculture may provide superior soil improvement and potential environmental benefits compared with organic farming because of the tillage requirement of organic farming (Trewavas, 2004). The need for long-term research has been advocated to assess the relative merits of conventional no-tillage agriculture compared with organic farming (Macilwain, 2004). There is little literature reporting such long-term comparisons. One 6-yr study in Pennsylvania showed that some form of primary tillage was required for crop yields in organic systems to match those in conventional systems, but that a pure no-tillage organic system was not viable (Drinkwater et al., 2000).

A long-term experiment, the Sustainable Agriculture Demonstration Project (SADP), was initiated at Beltsville, MD, to compare selected no-tillage grain cropping systems and a reduced-tillage organic system on a sloping, droughty site typical of the mid-Atlantic piedmont. The standard for comparison was a notillage system typical of that used in this area. Two additional no-tillage systems, one including winter annual cover crops and another including a perennial crownvetch living mulch, were compared with this standard for their potential to improve soil organic matter, reduce external inputs, and enhance environmental protection on erodible soils. Finally, an organic cropping system that reduced tillage to the minimum necessary for incorporation of manure and for weed control was included in this comparison. Performance of these systems during the first 4 yr of the experiment, which included transition years for the organic system, was reported by Teasdale et al. (2000). A simulation of projected yields, economic returns, and environmental impacts was reported by Watkins et al. (2002). This paper reports results from a comparison of these systems over a 9-yr period as well as a 3-yr uniformity trial that followed… <more>

(Source:  John R. Teasdale,* Charles B. Coffman, and Ruth W. Mangum, Agronomy journal-   http://naldc.nal.usda.gov/download/13149/PDF)

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Equipment Considerations for No-till Soybean Seeding

No-till planters and drills must be able to cut and handle residue, penetrate the soil to the proper seeding depth, and establish good seed-to-soil contact. Many different  soil conditions can be present at the time of planting  in the Mid-Atlantic region. Moist soils covered with
residue, which may also be wet, can dominate during late fall and early spring and occasionally in the summer. Although this provides for an ideal seed germination environment, such conditions can make it difficult  to cut through residue. In contrast, hard and dry conditions may also prevail. This is especially common when no-tilling soybean into wheat stubble during the hot, dry months of June and July. Although cutting residue is easier during dry conditions, it is more difficult to penetrate the hard, dry soils. Proper timing, equipment selection and adjustments, and management can overcome these difficult issues.

Two of the keys for success with no-till equipment are proper handling of the previous crop residue and weed control. If these issues are not considered, then the ability of the planter or drill to perform its functions is greatly limited. The residue has to be uniformly spread
behind the combine if the opening devices are going to cut through the material and plant at a uniform depth. It is very difficult for the planter/drill to cut the residue if the combine has left a narrow swath of thick residue and chaff. Ensure that the combine is equipped with a straw chopper and chaff spreader to distribute residue and chaff over the entire cut area.

For example, if a 30-foot platform header is cutting high-yielding small grain and dumps the material into a 5-6 foot swath, then this swath contains 5 to 6 times more material than the other cut area. The residue may vary from less than 30% coverage to more than 1-inch thick and can affect planting depth. This mat of material is an ideal place for disease and pest problems to accumulate and increases problems relating to cutting residue and penetrating the soil. This mat can create a lot of variability that makes it difficult to adjust the planter/drill for proper operation and this limits successful emergence and early crop growth.

Experience has shown that the residue is best handled by the planter/drill when the residue remains attached to the soil and standing. When the residue is shredded and chopped, it has a tendency to mat and not dry out as quickly as standing residue. The loose residue may not flow through the planter/drill as well and has potential to plug the opening devices.

The other key is weed control. In double-cropped soybeans, one of the reasons to convert to narrow rows is that crop canopy closure, which shades the weeds and gives the soybean more of a competitive advantage is faster. Due to the closure time, 7.5-inch rows may have
an advantage over 15- or 30-inch rows. However, if the weeds have a head start, this advantage can be lost. If standing weeds exist, you are asking the planter/drill to cut and move this extra material through the system, plus the crop has lost valuable resources of nutrients and water.

Probably the primary difference between conventional planter/drill systems and those designed for conservation tillage systems is weight. Since the openers and soil engaging devices must penetrate much firmer soils and cut the residue, the conservation planter/drill systems are built heavier and have the ability to carry much more weight than conventional systems. For adequate coulter penetration, weight may have to be added to the carrier. Some planter/drills use a weight transfer linkage to transfer some of the tractor weight to the coulters to ensure penetration. Because coulters are usually mounted several feet in front of the seed opening/placement device (in the case of coulter caddies even further), many use wide-fluted coulters, a pivoting hitch or a steering mechanism to keep the seed openers tracking in the coulter slots… <more>

(Source– http://pubs.ext.vt.edu/442/442-456/442-456_pdf.pdf)

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Green Manures – Effects on Soil Nutrient Management and Soil Physical and Biological Properties

Both organic and conventional growers can gain many benefits from increased use of green manures. A wide range of plant species can be grown as green manures as different ones can bring a variety of benefits. Leguminous plants will fix nitrogen from the air whilst non-legumes will conserve nitrogen by preventing nitrate leaching. Green manures add organic matter to the soil, improving its physical and biological properties and they can assist with pest, disease and weed management. Some of the effects on soil physical properties may only become significant after several green manure crops have been grown over a period of perhaps five to ten years. Green manures are often categorised according to the time of year they are grown.

Winter green manures or cover crops are usually sown in the autumn and incorporated in the following spring and may be legumes (e.g. vetch) or non-legumes (e.g. rye). Summer green manures are usually annual legumes (e.g. crimson clover) which are grown to provide a short term boost for fertility. However, they could also be nonlegumes (e.g. mustard).

Longer term green manures are usually pure clover or grass/clover leys grown for two or three years. They are common in organic stockless rotations where they form the main source of nitrogen. However, in conventional farming these rotations would be harder to justify unless there were animals to graze them.

Green manures may also be used in intercropping systems, although in vegetable cropping it is important to avoid too much competition with the cash crop. Protected cropping systems offer particular challenges and opportunities for green manuring whilst fertility building in orchards can be difficult as nitrogen must be provided at the right time to ensure good fruit set and crop quality. Green manures grown as an understory can also attract beneficial insects.

Green manures are often grown to add nitrogen to the soil. In organic systems this represents the main source of nitrogen, whilst for conventional growers, it can be a way of minimising fertiliser inputs. Almost all legumes use Rhizobia bacteria to fix nitrogen from the atmosphere.  Unfortunately finding out how much  nitrogen is actually fixed is not easy  and depends on many factors.  Firstly, the correct strain of bacteria  must be present. Different bacterial  species interact with different groups  of legumes (clovers, lucerne and trefoils, lupins, beans etc.). If the same types of plants are regularly grown then sufficient bacteria will usually be present to establish sufficient nodules. Sometimes it is worth inoculating the seeds with the correct type of bacteria. There are several types available commercially, at a modest cost.

Sometimes the nitrogen fixation still does not occur, even if the roots form a symbiosis with the bacteria. Some strains will infect the plant but not be very effective. They can even drain the plant of resources

<a href=http://www.organicadvice.org.uk/Factsheet%2024.10.pdf>more…</a>

(Source: Horticulture Development Company – http://www.organicadvice.org.uk/Factsheet%2024.10.pdf)

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Vegetation Control: Profitable Investment

Agricultural complex has always been one of the most significant propelling forces of the Ukrainian economy. Agriculture provides for 8% of total GDP, covering about 71% of the territory and 17% of working population employment. Meanwhile, in comparison with their foreign peers, the performance indicators of domestic agrarian companies demonstrate disproportion in operating results. Thus, if we scrutinize developing countries with agricultural specialization, Ukraine will demonstrate the highest rate of territory used for crop production (0.71 hectares per citizen) with a relatively small contribution to the GDP (-3−8% to the other countries level).

This correlation is reflected in the grain crop yield, which ranks Ukraine far behind the majority of leading grain-producing countries. Extensive management methods have led to the degradation of the black soil and thus – to the increase in expenses required per unit of cultivated area, fertilizers in particular.

It is impossible to improve  productivity performance in Ukraine without a substantial growth in mineral fertilizers use. However, global (reduction of the discrepancy between demand and production capacity) and domestic (deficit of phosphate fertilizers, potash fertilizer production standstill, increasing natural gas price) factors lead to constant rise in fertilizers prices for Ukrainian farmers and as a result lead to the profitable investments.

In addition, it should be noted that prices for agricultural machinery, fuel and pesticides have significantly increased. Even despite the fact that crop prices in 2011 have risen on average by 15%, the increase in prices for machinery and other accompanying expenses was more substantial.

The human capital costs have changed in a similar manner: the average nominal wage of an agricultural worker has increased by 25.9%during the last year. Still the Ukrainian agriculture industry employs +5-10% more population as compared to the European countries, while every employee produces 2-5x times less quantity of the added value.

In 2011 the expenses for fertilizers, POL and wages constituted almost 40% of crop production cost, thus during only one year the price jump caused spending spree by 15% and even more for specific crops. Let alone the cost of spare parts and materials for the repair of machinery and buildings (+0.3% in the total cost structure) and the rising costs for seed grain. In other words, such a scenario allows to catch up with the price increase tendency, but arouses the necessity to renovate obsolete production capacities, agriculture machines fleet, which also become less affordable, especially in terms of “price-quality” ratio. For example, some items from leading international equipment producers which are available in Ukraine have raised in price by 10% and even more in comparison with the analogue machines from the CIS.

Thus the problem is to find internal sources of production cost reduction to compensate the cost increase, which is beyond the agricultural holding control (fuel and metal prices, fertilizers deficiency, etc.). Global agricultural products prices do not depend on production cost in Ukraine, so it is the agrarian’s business to reduce it by cultivation cost-cutting. For this reason, developed countries opt for the use of precision agriculture system based on computer analysis of remote crops sensing data (RSD). Some Ukrainian farms already use such agricultural technologies as geologic information systems (GIS) and global positioning system (GPS). But in such a limited format these technologies are rather used to control equipment fleet maintenance, fuel input rationality and adequate farm maps creation. In the course of our cooperation with the agricultural companies management, we have discovered that a maximum allowable innovation is considered to be the purchase of expensive foreign equipment, its GPS monitoring installation and the creation of interactive maps of soils of rather satisfying quality. But precision agriculture implies exactly the efficient usage of every single asset. Even a large fleet of tractors can not effectively cultivate the fields without additional instructions on problem areas, non-rational heavy fertilization can be harmful and interactive maps do not allow to understand the current field condition in a real-time mode. It proves to be a real problem for the large farms as they simply fail to control the vegetation on their fields, and thus to identify in time the causes of low crop yield in different regions.

The main condition for the high-efficient GPS and GIS deployment is close cooperation with the system of constant remote vegetation control of field crops. Altogether, this forms the organizational strategic units . This scheme enables to attract fewer workers to control vegetation, field works planning and maintenance of communication between individual units and subunits of agro-enterprises. The vegetation control system performs constant monitoring of agro-enterprises soils irrespective of the distance among the fields and of the crops planted. Upon the abnormal “spot” appearance on the field, the person in charge receives a message and the agronomist makes appropriate decisions regarding fertilization, irrigation or other cultivation arrangements. We have to admit that other methods of soil monitoring (driving around the fields, installation of special observing equipment on certain areas, taking soil pieces for laboratory analysis, etc.) are less informative but consume more time and funds. In addition, each of the observations is far more difficult to organize and to hold than to download all required current and historical data (with its automatic interpretation) from any computer connected to the Internet.

As the conducted research have revealed, the cost of the each service on average starts from $1.5 a year per hectare, depending on the total farm area the system maintains. At the same time, this service allows to save $3-5/ha and to make profit from efficiency performance increment (e.g. for winter wheat) starting from $13/ha. In other words, every invested dollar gives an opportunity to earn 18 times more by reducing costs and increasing the efficiency of crops cultivation. For instance, if 10 largest domestic agricultural holdings have a land domain of more than 150,000 ha each, the application of RSD satellite analysis will bring a profit measured in tens of millions US dollars.

Nowadays, only a few domestic companies in Ukraine render services of crop vegetation control. However this derives from a low demand due to conservatism of the most agrarians, their general aversion to high technologies and short period of presence of such services in the domestic market. On the other hand, internal trends in the agricultural sector indicate that the next steps of the businessmen in agricultural field will become optimization of assets and search for sources to improve their operation efficiency.

Tags: satellite, precise agriculture, GIS, GPS, crop, Ukraine, efficiency, wheat, sunflower, barley, legumes

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Weather History

To get data on historical temperature figures, cloudiness and humidity indices for each month in different regions or countries all over the world you can use World Meteorological Organization database  by following this link.

Moreover, some crop monitoring systems (e.g. satellite vegetation monitoring systems) offer the option of precise weather forecast backed by historical database

Tags: World Meteorological Organization, forecast, weather, agriculture, temperature, cloudiness, humidity, crop, yield

 

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Precision Agriculture and the Ukrainian Reality

The Committee on World Food Security research shows that nowadays global welfare largely depends on the dynamics of food production. We have learned how to synthesize scarce sapphire crystals and how to replace expensive petrol with biofuel, but still unable to cope with the hunger problem. The world population is growing, while the area of free land for the expansion of crops is limited – another deforestation or swamps draining is a potential threat of ecological disaster. Profits from sunflower oil, wheat or sugar sales in the world market are comparable to machinery and coal trade profitability. The question is why more than a third of Ukrainian agricultural enterprises are unprofitable in such favorable environment? What methods of doing business help global leaders of the agricultural market work more efficiently than domestic companies that have rich Ukrainian soils?

Ukrainian-type Efficiency of Agricultural Business

The necessity to pursue the way of the intensive agricultural development became evident to the most developed countries long ago. The most recent developments in science and technology are applied not only to space rocket engineering, but to work in the field as well. Modern agricultural machinery is equipped with computers, new varieties of crops are grown in the laboratories, whilst satellites and drones are watching crops of large landowners in real-time. Nowadays agriculture of developed countries turns to an absolutely new level of competition – the efficient one. In a market where you can not control the price, you must manage the prime cost or go away. Agricultural market has become so global that the most effective way to manage profitability is to manage production costs. Modern wars are held without tanks and infantry – just one rocket is enough if it hits the enemy’s camp navigated from space and powered with minimum resources, but reaching maximum effect. The same processes take place in the agrarian sector. All the efforts have been turned to allocate available resources with the highest efficiency to achieve the utmost result.

The general picture of precision agriculture in Ukraine calls for new efficient reforms at least to overtake the leading world agrarian producers. Let us turn to statistics. According to the State Statistics Service of Ukraine, since the proclamation of independence in Ukraine the level of the plowed area reached nearly 72% of total country territory which is one of the highest indices value in the world. At the same time the production volume of cereals/legumes and sugar beets per capita declined in comparison with 1990 by 13% and 65% respectively. This means that the extensity of using acreage has not justified itself. The same is about the excessive use of another available resource in Ukraine – manpower. In this country over 16% of the population is employed in the agricultural sector (according to the FAO research, this figure does not exceed 9% for the developed countries), but the number of added value created by one employee is “only” 2,500 dollars per year (in the U.S. it equals to 51,000 dollars, in Romania – 9,700, in Poland – 3,000).

Significant gap between leading countries and Ukraine can be actually closed only with the help of many millions of investments that seems very difficult within the global financial crisis. The lack of “long-term money” (the payback period in the agriculture reaches at least 5-7 years even in the most optimistic scenario) multiplied by the lack of investments defend guarantees, inflation and instability of commodity markets, creates difficulties to obtain financing even for the largest agricultural holdings. However, it is hardly possible to overtake the world leaders by the other way in the current business environment: according to the World Bank, Ukrainian reality shows us that Ukraine has one of the lowest rates of fertilizer expenses and tractor use per unit of cultivated area, the average productivity depending on the crop is lower than world analogs in two or three times, each planting and harvesting campaign has deficiency of oil products whose production in Ukraine is insufficient because of a lack of raw materials and general equipment deterioration.

Let’s Change the Principles!

In such a situation the Ukrainian agricultural sector needs to find alternative ways for the further development. It’s half of the problem when we lose our export positions – much worse is when we have to purchase the agricultural production for foreign currency abroad like it regularly happens with sugar. If oil, natural gas, phosphates and other raw materials for agriculture are rising in price, it is better to optimize their consumption per unit of cultivated area to achieve maximum efficiency in each field, for each type of crops. The one who will be able to offer worthy quality at a reasonable market price, without doing himself out of his share and his interest as an entrepreneur, can be a winner in the market competition. It is necessary to abandon an unprofitable principle of explicit loss and to switch to smart management “as needed”. What’s the use of distributing fertilizers evenly, if only few fields or areas within the field need more fertilizers, while the others have the surplus? Does it make sense to go around the crops every day to check whether everything is in order, if there are systems for identifying problem fields? Why do we use the weather forecast for the nearest settlement, if we need the weather for a particular field in two dozen kilometers away? These are the questions which have become the philosophy of agribusiness in developed countries but are not that popular among the Ukrainians.

Precision Agriculture

It is possible to achieve the result described above with the help of so-called “precision agriculture” – the use of the concept about the existence of heterogeneity within a single field or planting. Such features could be caused by the landscape specifics, soil composition and proximity of mineral layers, condition of groundwater, climatic characteristics and features of crops which were grown on the area before. Precision agriculture foresees the continuous monitoring of crops and soil for the operational planning of the range of actions to optimize the condition of problem areas. For example, if a separate section of the field area of 20 hectares has a small yellow spot area of 0.5 hectares, it is not necessary to fertilize or to impose additional watering sessions to the whole field – it is enough just to handle problem areas. This will result in much lower costs of fertilizer, POL, wages and depreciation of equipment, even more – it will save working hours of equipment and employees for other tasks.

Monitoring Systems

 Monitoring of fields can be realized in different ways: driving round fields, collecting and analyzing soil samples, using sensors and aerial photography. At the current level of technological development, one can launch aircraft without a pilot but equipped with sensors,  photo- and video cameras and filled with fuel to make a 30-minute flight. However, the complexity of control and maintenance of such equipment, as well as the size of field (over 100 hectares) make this work scheme quite expensive and hardly feasible. For such a scale, agrarians opt for satellite space shooting , the processing of which allows to monitor crops and to make decisions about pointed application of fertilizers, insecticides or herbicides, irrigation or other actions  based on the handling of images with overlaid in red and infrared spectrum. In addition, data from such programs can be uploaded in any electronic device or in the onboard computer of agricultural machinery making it easier to set tasks for employees in the agricultural enterprise.

Satellite crop monitoring systems are successfully used in many countries of America, Europe and the CIS. The most well-known and effective providers of this service are such companies as Cropio (USA/Germany), Astrium-Geo (France), Mapexpert (Ukraine), Vega (Russia). The use of these systems allows not only to monitor efficiently the condition of fields, but also to receive reports and notifications about the most important issues through Internet or sms, to make forecasts of the field productivity and the entire enterprise, to receive related information about the agricultural markets, currency rates and prices for agricultural products in certain markets, to compare current and historical indices of vegetation, soil moisture, content of fertilizers.

Cost Savings Plain to See

Few of us has thought that it takes at least 0.4 liters of fuel or UAH 1.2 to drive round of field area of 1 ha (100 m*100 m) 8 times per year. According to the American Institute of the Precise Farming, the differentiated fertilization brings savings of 10% per hectare. Having summed these and other explicit and implicit costs, we can obtain savings of at least UAH 146 per hectare using satellite observation in Ukrainian agriculture.

If in the Ukrainian realities domestic businessmen are progressive enough and ready to start running the management according to new standards using the techniques of precise agriculture, it is quite possible that eventually Ukraine will become one of the absolute world leaders in the production of some crops, and a number of major agricultural exchanges will be opened on its territory involving customers from around the world. The Ukrainian agronomist who uses services of satellite crop increases its professional efficiency and management methods makes a real “jump” from the Stone Age to the age of high technology. Such an agronomist is in the same league with his colleagues from around the world leveraging not only Soviet scientific school knowledge, but also the global scientific progress.

The result is smaller staff of agronomists, lower fuel and fertilizer costs. Having one or more of such satellite monitoring centres, the agrarian can cut costs that previously put at risk the profitability of enterprise, and what is even more crucially, optimize the quality and return of each resource, be it land, workers, machinery or fertilizers. It is always better to make qualitative changes rather than quantitative ones in each operation of business cycle. The customer is ready to buy the product at a price not higher than a certain threshold which occurs as the average price of all sellers, in a free market he will not overpay for our inability to run a business efficiently. As advertising recalls: “Why should I pay more?”

Summing up, let us recollect another wise saying: “Everything dies without sustained development.” Nowadays it is not enough to own hundreds of hectares of high-quality black soil or endlessly increase fleet of vehicles. Once in a while one should step back and take a look at unproductive attempts to invest and think how to make more money. The authors of political economy put it that the possessing of right information helps to make a profit.

Tags: precise agriculture, Ukraine, satellite, fertilizer, wheat, barley, legumes, sunflower, agronomist, Cropio

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