Most crop models make use of a nutrient-balance approach for modelling crop response to soil fertility. To counter the vast input data requirements that are typical of these models, the crop water ...productivity model AquaCrop adopts a semi-quantitative approach. Instead of providing nutrient levels, users of the model provide the soil fertility level as a model input. This level is expressed in terms of the expected impact on crop biomass production, which can be observed in the field or obtained from statistics of agricultural production. The present study is the first to describe extensively, and to calibrate and evaluate, the semi-quantitative approach of the AquaCrop model, which simulates the effect of soil fertility stress on crop production as a combination of slower canopy expansion, reduced maximum canopy cover, early decline in canopy cover and lower biomass water productivity. AquaCrop's fertility response algorithms are evaluated here against field experiments with tef (Eragrostis tef (Zucc.) Trotter) in Ethiopia, with maize (Zea mays L.) and wheat (Triticum aestivum L.) in Nepal, and with quinoa (Chenopodium quinoa Willd.) in Bolivia. It is demonstrated that AquaCrop is able to simulate the soil water content in the root zone, and the crop's canopy development, dry above-ground biomass development, final biomass and grain yield, under different soil fertility levels, for all four crops. Under combined soil water stress and soil fertility stress, the model predicts final grain yield with a relative root-mean-square error of only 11–13% for maize, wheat and quinoa, and 34% for tef. The present study shows that the semi-quantitative soil fertility approach of the AquaCrop model performs well and that the model can be applied, after case-specific calibration, to the simulation of crop production under different levels of soil fertility stress for various environmental conditions, without requiring detailed field observations on soil nutrient content.
It was observed that around 1300000 Crop Cutting Experiments (CCE) were conducted in India every year to find out the crop yield estimates of several major and minor crops under General Crop ...Estimation Surveys (GCES). Due to shortage of manpower and huge bulk of work day by day the data quality is becoming questionable. To tackle this problem, a pilot study was conducted by ICAR-IASRI, New Delhi sponsored by Directorate of Economics and Statistics (DES), Ministry of Agriculture and Farmers Welfare (MoA & FW), Govt. of India to generate district level estimates of major crop yield from a reduced sample size of villages selected from the states. With the reduction in number of villages, the problem of no sample size in some districts were faced during the study where common design based estimates of crop yield cannot be generated. To tackle this problem Aggregate level Small Area Estimation (SAE) was used to tackle this problem. The results obtained from this study in the state of Uttar Pradesh for two major crops, i.e. rice and wheat for two seasons, i.e. kharif and rabi of Agriculture Year 2015-16 and for paddy in Assam for kharif of the Agriculture Year (AY) 2015-16 in India were discussed. The yield estimates were compared with the estimates released under GCES for AY 2015-16. It was found that the estimates obtained from reduced sample size of number of CCEs w.r.t. GCES, produced similar estimates with acceptable level of precision.
► Integrated ANNs were present to estimate monthly river flow and the models. ► Integrated ANNs can explore spatial variation in rainfall and evaporation distribution. ► Integrated ANNs’ performance ...was compared with that of lumped ANN and local linear regression model. ► Integrated ANNs perform well to estimate the monthly streamflow than other models.
Streamflow model including rainfall–runoff and river flow models play an important role in water resources management, especially in arid inland area. Traditional conceptual models have the disadvantage of requirement of spatial variation parameters about the physical characteristics of the catchments. To overcome this difficulty, in this study, several integrated Artificial Neural Networks (ANNs) were presented to estimate monthly river flow, and the models include the semi-distributed forms of ANNs that can explore spatial variation in hydrological process (such as rainfall distribution and evaporation distribution) and no requirement of physical characteristic parameters of the catchments. In an arid inland basin of Northwest, integrated ANNs were developed using hydrological and agricultural data, and its performance was compared with that of lumped ANN and local linear regression model (LLR). Results showed that the integrated ANNs perform well to estimate the monthly streamflow at outlet of mountain with Root Mean Square Error (
RMSE) of 0.36
×
10
7
m
3 and Relative Error (
RE) of 9%. Similarly, the integrated ANNs can also accurately estimate the monthly river flow downstream of the basin with
RMSE of 0.35–0.38
×
10
7
m
3 and
RE of 22–27%. When compared with integrated ANNs, the lumped ANN and LLR models have lower precision to simulate monthly streamflow in arid inland basin. Presented integrated ANN models retain the advantages of the semi-distributed models considering the heterogeneity and spatial variation of hydrological factors and the physical characteristics in the catchment, while taking advantage of the potential of ANNs as an effective tool in nonlinear mapping or functional relationship establishment. In contrast to traditional models either in the lumped ANN or in empirical regression forms, the new approach of integration of Artificial Neural Networks has shown great potential in streamflow modeling.
This article proposes the use of moment functions and maximum entropy techniques as a flexible approach for estimating conditional crop yield distributions. We present a moment-based model that ...extends previous approaches, and is easily estimated using standard econometric estimators. Predicted moments under alternative regimes are used as constraints in a maximum entropy framework to analyze the distributional impacts of switching regimes. An empirical application for Arkansas, Mississippi, and Texas upland cotton demonstrates how climate and irrigation affect the shape of the yield distribution, and allows us to illustrate several advantages of our moment-based maximum entropy approach.
Cropland fires are an important source of black carbon (BC) emissions. Previous research has suggested that springtime cropland burning in Eastern Europe, more specifically Russia, is a main ...contributor of BC in the Arctic atmosphere, acting as a short-lived climate forcer strongly influencing snow-ice albedo and radiation transmission. BC emissions from cropland burning were estimated for the Russian Federation for years 2003 through 2009 using three satellite fire products, the 1 km MODIS Active Fire Product, 0.5° MODIS Fire Radiative Power monthly climate modeling grid product, and the 500 m MODIS Burned Area Product, and a agricultural statistics approach based on a modified method developed and published by the All-Russian Institute of Organic Peat and Fertilizers to estimate farm- and regional-level residue loading from straw surplus left after grain harvesting, while accounting for agricultural management and agrometeorological inputs. The satellite-based emission calculations utilized several different land cover classification schemas for defining croplands in Russia for both the 1 km MODIS Land Cover Product and the 300 m MERIS GlobCover v2.2 data sets. In general, the peaks of BC emissions from cropland burning occurred during the spring (April–May), summer (July–August), and the fall (October). 2008 had the highest annual BC emissions. The range of average annual BC emissions from cropland burning calculated from the different satellite fire products was 2.49 Gg–22.2 Gg, with the agricultural statistics approach annual average equal to 8.90 Gg. The Global Fire Emissions Database (GFED) version 3 reported an annual average of 11.9 Gg of BC from agricultural burning. The results from this analysis showed that the majority of BC emissions originated in European Russia, followed by smaller contributions from West Siberia, Far East Russia, and East Siberia macro-regions. An uncertainty assessment on data used to calculate the BC emissions found moderate uncertainty in some of the input data used in this first attempt to produce spatially and temporally explicit BC emission estimates from cropland burning in the Russian Federation.
► Quantified cropland fire and black carbon emissions for the Russian Federation. ► Peaks of BC emissions occurred in the spring, summer, and fall. ► Highest BC emissions occurred during spring in European Russia and West Siberia. ► The range of average annual BC emissions was 2.49 Gg–22.2 Gg.
Most off-the-shelf basic methodological tools currently used in pastoral development (e.g. technical definitions and conventional scales of observation) retain underlying assumptions about stability ...and uniformity being the norm (i.e. 'equilibrium thinking'). Such assumptions reflect a theoretical framework which had been questioned since the 1970s and was openly disproved in scientific circles during the 1990s, when it was shown to be fundamentally inadequate. Today, lingering equilibrium assumptions in the methodological legacy of pastoral development get in the way of operationalising state-of-the-art understanding of pastoral systems and drylands. Unless these barriers are identified, unpacked and managed, even increasing the rigour and intensity of data collection will not deliver a realistic representation of pastoral systems in statistics and policymaking. This article provides a range of examples of such 'barriers', where equilibrium assumptions persist in the methodology, including classifications of livestock systems, conventional scales of observation, key parameters in animal production, indicators in the measurement of ecological efficiency, and the concepts of 'fragile environment', natural resources, and pastoral risk.
This paper examines the impact of climatic change on the level of total agricultural production of Sub-Saharan Africa (SSA) and non-Sub-Sahara Africa (NSSA) developing countries. In doing so it uses ...a new cross-country panel climatic dataset in an agricultural production framework. The results show that climate, measured as changes in country-wide rainfall and temperature, has been a major determinant of agricultural production in SSA. In contrast, NSSA countries appear not to be affected by climate in the same manner. Simulations using the estimates suggest that the detrimental changes in climate since the 1960s can account for a substantial portion of the gap in agricultural production between SSA and the rest of the developing world.
The aim of this paper is to explore the development of agricultural statistics in the Kingdom of Yugoslavia in 1918-1941 period. Collecting statistical data on agriculture was a duty of the Ministry ...of Agriculture. The paper reveals how the Ministry performed this job, which data were collected and in which publications these data were published. The development of agricultural statistics has been analysed by applying historical method. Comparison with agricultural statistics of certain European countries has been made by applying comparative method. It has been concluded that agricultural statistics in the Kingdom of Yugoslavia recorded significant improvements during the interwar period: all relevant data were collected, international standards were met, and publications were modernized. As for the manner of conducting statistical research and presentation of statistical data, they were in the spirit of the time and responded to the achieved level of development of statistics in the Kingdom of Yugoslavia.
The South–North Water Transfer (SNWT) project (upon completion) will deliver some 4.8 billion m
3 of water per annum to Hebei, Beijing and Tianjin — greatly mitigating water shortage in North China. ...Surface water that is currently restricted to urban use could then become partly available for agricultural production. This will reduce the dependence of agriculture on groundwater, which will in turn retard groundwater depletion in the region. This study determines the spatial and temporal distributions of agricultural water requirement in Hebei Plain. This in turn lays the basis for surface water reallocation following the completion of the SNWT project. DSSAT and COTTON2K crop models are used along with crop coefficient methods to estimate required irrigation amounts for wheat, maize, cotton, vegetables and fruit trees in Hebei Plain. The study uses 20 years (1986–2006) of agronomic, hydrologic and climate data collected from 43 well-distributed stations across the plain. Based on the results, wheat accounts for over 40% of total irrigation water requirement in the plain. Similarly, wheat, maize and cotton together account for 64% of the total irrigation water requirement. The piedmont regions of Mount Taihang have the highest irrigation requirement due to high percent farm and irrigated land area. The months of April and May have the highest irrigation water requirement, respectively accounting for 18.1% and 25.4% of average annual irrigation. Spatial and temporal variations in our estimated irrigation water requirement are higher than those in the officially published statistics data. The higher variations in our results are more reflective of field conditions (e.g. precipitation, cropping pattern, irrigated land area, etc.). This therefore indicates a substantive improvement (in our study) over the average statistical data. Based on our simulation results, viable surface water reallocation strategies following the completion of the SNWT project are advanced and discussed.
This article presents a medium resolution land use data set (5 arc min, c. 10 × 10 km) for the year 2000 that reproduces national land use statistics for cropland and forestry at the country level. ...We distinguish five land use classes displayed as percent-per-gridcell layers: cropland, grazing, forestry, urban and infrastructure areas, and areas without land use. For each gridcell, the sum of these five layers is 100%; that is, the Earth's total land area is allocated to these five classes. Spatial patterns are derived from available thematic maps and reconciled with national extents from census data. Statistical comparisons of the resulting maps with MODIS and CORINE data demonstrate the reliability of our data set; remaining discrepancies can be largely explained by the conceptual difference between land use and land cover. The data set presented here is aimed to support the systematic integration of socio-economic and ecological data in integrated analyses of the coupled global land system. The data set can be downloaded at
http://www.iff.ac.at/socec/
.
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