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A Peanut Simulation Model: I. Model Development and Testing
Agronomy Journal
Abstract
A biophysically robust crop simulation model can assist industry planning and farmer decision-making via simulation analyses to quantify production potential and production risks. Accordingly, we developed a simple, yet mechanistic peanut simulation model for use in assessing climatic risks to production potential for both irrigated and dryland conditions. The model simulates pod yield, biomass accumulation, crop leaf area, phenology, and soil water balance and is suitable for application over a diverse range of production environments. The model uses a daily time step, utilizes readily available weather and soil information, and assumes no nutrient limitations. The model was tested on numerous data from experiments spanning a broad range of environments in the tropics and subtropics. The model performed satisfactorily, accounting for 89% of the variation in pod yield on data sets derived from independent experiments, which included crops yielding from 1 to 71 t ha⁻¹. Limitations of the model and aspects requiring better understanding to improve quantification are discussed. Despite some limitations, the model attains a useful degree of predictive skill for a broad range of situations and environments. This outcome is testimony to the utility of the simple, generic framework used as the basis for this model. The model is suitable for simulation studies aimed at assisting industry planning and farmer decision-making.
Contribution from the Agricultural Production Systems Res. Unit, Queensland Dep. of Primary Industries, and the Commonwealth Scientific and Industrial Res. Org., P.O. Box 102, Toowoomba, QLD 4350, Australia.
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... Muchow (1989) and Siddique and Sedgley (1986). Hammer et al. (1995) found that dHI/dt varied among experiments in peanut. They related this variation to the average temperature from sowing to the end of leaf growth. ...
... The chickpea crop model of Soltani et al. (1999) was used in this study. This model is similar to the models of Sinclair (1986) and Hammer et al. (1995). The model is based on a daily time step and simulates crop growth and development as a function of temperature, solar radiation and water availability. ...
... Owing to the lack of the data to quantify this effect, it is modeled by stopping the linear increase in HI if FTSW is below 0.1 and HI exceeds 0.2 as outlined by Chapman et al. (1993), Hammer et al. (1995) and Hammer and Muchow (1994). The minimum value for HI of 0.2 represents the maximum remobilization of stored assimilates. ...
The linearity of harvest index (HI) increase has been used as a simple means to analyze and predict crop yield in experimental and simulation studies. It has been shown that this approach may introduce significant error in grain yield predictions when applied to diverse environments. This error has been ascribed to variability in the rate of linear increase in HI with time (dHI/dt). Data from two field experiments indicated in chickpea (Cicer arietinum L.) that dHI/dt varied among sowing dates. This variation was related to the length of pre-seed growth phase and vegetative growth (dry matter production) during this phase and could be described by the mean daily temperature from sowing to beginning seed growth. dHI/dt increased linearly with increase in the temperature up to 17 8C when it reached to its maximum value and remained constant. Simulation of these field experiments using a chickpea crop model including a constant dHI/dt resulted in yield over-prediction for some sowing dates. However, a modified HI-based approach greatly improved model predictions. In this approach, potential seed growth rate (SGR) is calculated using the linear HI concept, but actual SGR is limited to current biomass production and the remobilisation of dry matter accumulated in vegetative organs before the seed growth period. This modification well accounted for temperature and drought effects on HI and resulted in better yield predictions under conditions of major chickpea producing areas of Iran. Therefore, we recommend that the modification to be applied in the other HI-based models. #
... In agreement with previous studies (Craufrud et al., 2002;Hammer et al., 1995), we verified that the increase in HI POD was linear over time (Fig. 1d), and registered daily rates of HI POD were consistent with those reported for peanut in the literature (Bennett et al., 1993;Craufrud et al., 2002;Hammer et al., 1995;Stirling and Black, 1991). Final HI POD and HI of seeds were lower for CEGH than for CPGH (Table 3) due to the anticipated maturity of the former. ...
... In agreement with previous studies (Craufrud et al., 2002;Hammer et al., 1995), we verified that the increase in HI POD was linear over time (Fig. 1d), and registered daily rates of HI POD were consistent with those reported for peanut in the literature (Bennett et al., 1993;Craufrud et al., 2002;Hammer et al., 1995;Stirling and Black, 1991). Final HI POD and HI of seeds were lower for CEGH than for CPGH (Table 3) due to the anticipated maturity of the former. ...
... We assume that these negative responses in CEGH observed in Year 2 would be associated with negative effects of high temperatures reported in the literature, which indicate that short or prolonged periods of high temperature during reproductive development of peanut are known to cause significant yield losses (Ketring, 1984;Vara Prasad et al., 2000b;Wheeler et al., 1997). Hammer et al. (1995) indicated a reduction of HI in the Cv Early Bunch exposed to temperatures above 28 • C. Likewise, high temperatures during preflowering and flowering periods significantly reduced the fruit-set in peanut (Vara Prasad et al., 1999a,b, 2000a,b, 2001 and, therefore, HI. The analysis of responses to above-optimum temperature (i.e. ...
An important milestone in Argentina peanut (Arachis hypogaea L.) breeding was the shift in the 1970s from cultivars with erect growth habit (CEGH) to cultivars with procumbent growth habit (CPGH). CPGH improved seed yield but also lengthened growth cycle. However, there is no information if the change in growth habit (GH) may have involved a phenotype with a canopy architecture that makes a differential capture and use of resources. Field experiments were performed to compute leaf are index (LAI), the fraction of incident photosynthetically active radiation intercepted by the crop (fIPAR), biomass production, radiation use efficiency (RUE) and harvest index (HI). Four cultivars of each GH, released between 1948 and 2004, were evaluated. The LAI was always larger among CPGH than among CEGH. Only the former reached the critical LAI. Likewise, fIPAR of CPGH was higher than that of CEGH throughout the crop cycle. Maximum fIPAR differed between GHs (P < 0.001), with interannual mean values of 0.95 for CPGH and 0.77 for CEGH. Final total biomass of CPGH was 37% larger than that of CEGH. RUE values ranged between 1.88 and 2.46 g MJ⁻¹, and differed significantly (P ≤ 0.008) between GHs (CEGH > CPGH), Years (Year 1 > Year 2) and GH × Year (CPGH Year 1 > CPGH Year 2 = CEGH Year 2 = CEGH Year 1). CPGH improved pod yield (+64%), seed yield (+101%), HI of pods (+29) and HI of seeds (+56%) respect to CEGH. Considering the effects of GH on the capacity of cultivars for achieving the critical LAI with current crop management, future research should focus on alternative sowing patterns (e.g., reduced row spacing among CEGH).
... Temperature has been shown to be a major driver of key phenological events in peanut, and hence closely influences the total growth period (Kelleher, 1992). Growing degree days (GDD), or thermal time, has been shown to be a highly accurate tool for the prediction of these phenological events and is calculated by the number of degrees that the daily mean temperature exceeds a set base temperature (Robertson et al. 2002, Hammer et al. 1995, Leong and Ong, 1983, Awal and Ikeda, 2002, and Dreyer et al. 1981. There are three assumptions that exist for this heat sum or thermal time calculation in peanut. ...
... Increasing pressure for primary producers to increase yield and yield quality, while reducing crop size and agronomic inputs, has led to the development of more robust on farm decision support systems which utilise crop simulation models. Simulation models have been developed to identify the production potential and risks of a multitude of crops including rice (Angus et al. 1996), canola (Morgan et al. 1999), cotton (Jones and Barnes, 2000), soybean (Paz et al. 2004), sorghum (Hammer and Muchow, 1991), wheat (Weir et al. 1984) and peanuts (Boote, 2004, Hammer et al. 1995, and Robertson et al. 2002. The major production risk to all crops is climatic variability, with irrigated crops suffering mainly from radiation and temperature variations, in the absence of nutrient and biotic limitations, while dryland crops have the added stress of variations in available soil water (Hammer et al. 1995, Robertson, et al. 2002, Porter, 2000. ...
... Simulation models have been developed to identify the production potential and risks of a multitude of crops including rice (Angus et al. 1996), canola (Morgan et al. 1999), cotton (Jones and Barnes, 2000), soybean (Paz et al. 2004), sorghum (Hammer and Muchow, 1991), wheat (Weir et al. 1984) and peanuts (Boote, 2004, Hammer et al. 1995, and Robertson et al. 2002. The major production risk to all crops is climatic variability, with irrigated crops suffering mainly from radiation and temperature variations, in the absence of nutrient and biotic limitations, while dryland crops have the added stress of variations in available soil water (Hammer et al. 1995, Robertson, et al. 2002, Porter, 2000. Simulation models can identify how a crop will be affected throughout a season by comparing current climatic conditions with historical climate data and then identifying optimum farming practices to produce high yielding and best quality crops. ...
The use of multispectral remote sensing technologies for yield prediction and in- field stress detection within the agricultural industry is not a new concept. However, the adoption of hyperspectral technologies and the identification of distinct spectral bands from growing leaves for disease identification and crop maturity assessment have not been widely developed. Within peanuts, the determination of maturity and the presence of aflatoxin within kernels through the visible and ultra violet spectral range have been previously studied. However, the measurement of these traits within the near infrared spectral region has been limited, with even less research on their indirect measurement through the peanut shell or leaf. The research presented in this thesis examined field spectroscopy and laboratory based near infrared (NIR) analysis as a method of identifying aflatoxin contamination and maturity variation within peanut kernels via in- field leaf reflectance measurements as well as the more direct absorbance measurement of dried and ground kernel, shell and leaf samples. To assess the possibilities of using other spectral technologies, aerial infrared (IR) and satellite multispectral images were also acquired and analysed to determine if correlations existed between the spatial variations of IR reflectance of in-field peanut canopies with the spatial variability of pod maturity and incidence of aflatoxin. The ability of this technology to predict peanut pod yield was also investigated, as this is difficult due to the indeterminate flowering pattern and underground podding habit of the peanut plant.
For the direct prediction of kernel maturity, the direct absorbance measurement of dried and ground peanut kernels using NIR spectroscopy, identified a 90% explanation of variance in differentiating kernels into immature, mid mature and mature classes, using partial least squares regression (PLS-R), and the differentiation of immature from mature kernels, using partial least squares discriminant analysis (PLS-DA). Both methods of statistical analysis identified the specific wavelengths 456, 1414, 1726, 2140 and 2310 nm as significant in the spectral prediction models. Indirect prediction of kernel maturity was also attempted using the absorbance spectra of ground dried shell and leaf samples. For dried and ground shell absorbance spectra, both PLS-R and PLS-DA analysis produced explanations of variance of greater than 80% with the wavelengths 1368, 1420, 1670, 1722, 2266 and 2336 nm identified as being significant. For dried leaf samples, explanations of variance exceeding 80% following PLS-R analysis were identified from the spectral absorbance data for the prediction of kernel maturity with the specific wavelengths of 1486, 1508, 1782, 1958 and 2056 nm identified as being significant. The strong correlation identified from the absorbance characteristics of dried leaf samples and kernel maturity was also found by the in- field reflectance measurement of growing leaves with field spectroscopy. Explanations of variance greater than 90% were identified from both irrigated and dryland field trials, using PLS-R analysis, with the wavelengths 640, 656, 660, 747, 964, and 1124 nm found to be significant. As well as the hyperspectral analysis of growing leaves, significant correlations (up to r= 0.73**, P=0.006) were also identified in the prediction of pod maturity with canopy reflectance using brightness values from single band (IR) aerial imagery and normalised difference vegetation index (NDVI) derived values from high resolution multispectral satellite imagery.
For aflatoxin prediction within dried and ground peanut kernels, up to 83% of the variation was explained following PLS-DA of absorbance data from the laboratory based NIR, and up to 63% of the variation explained in the prediction of actual aflatoxin concentration within the kernel following PLS-R analysis. The specific wavelengths of 480, 636, 926, 1124, 1240, and 1734 nm were identified from both statistical methods as being significant in the predictive model. High explanations of variance, of up to 70% were also identified in the discrimination of the absorbance data from dried and ground peanut shells removed from aflatoxin contaminated and aflatoxin free kernels, with the wavelengths 480, 530, 935, 1096, 1102, 1726, 1960 and 2306 nm identified as significant. No correlation was identified between the absorbance spectra of dried ground leaf samples with the presence of aflatoxin within pods attached to the plant in which the leaf samples were removed. This however was not the case with in- field spectrometer reflectance measurements, where strong explanations of variance of up to 80% were shown to be able to differentiate the reflectance spectra of growing leaf samples from plants with pods inoculated with the aflatoxin producing Aspergillus flavus spores. Furthermore PLS-R analysis was able to predict the actual concentration of aflatoxin within the pod via the spectral reflectance of the growing canopy, producing an explanation of variance of up to 78%. The wavelengths 480, 935, 1124 and 1726 nm were shown by both methods of statistical analysis to be significant in the discrimination of plants with pods contaminated with aflatoxin. A correlation was also identified between low IR reflectance regions of a peanut crop and high aflatoxin risk zones, using aerial IR and multispectral satellite imagery. These findings were hypothesised to be a result of stressed regions of the crop having smaller or diseased canopies with reduced soil shading and hence were more likely to experience the high soil temperature and low soil moisture conditions favoured for aflatoxin production.
Strong correlations were shown between canopy reflectance, either measured as brightness values (aerial IR) or NDVI value (satellite), and pod yield (up to r= 0.92**, P= 0.000). This strong correlation was hypothesised to be a result of a strong association with harvest index, where regions of the crop exhibiting higher IR reflectance were most commonly attributed with bushes of larger biomass or less stress, and therefore had more capacity to produce larger yields.
This research has clearly demonstrated the ability of non- invasive hyperspectral and multispectral technologies to predict kernel maturation and the incidence of aflatoxin directly from changes in the absorbance characteristics of kernel constituents. It has also shown a similar predictive ability through changes in the constituents of peanut shell, as well as the reflectance properties of growing leaves. The identification of specific wavelengths which correlate with valuable commercial traits, could lead to the future development of light emitting devices (LED) or similar sensors that when installed within in-line sorting machines may allow a more accurate removal of aflatoxin affected produce, or alternatively be incorporated into hand held devices for the rapid assessment of peanut loads at delivery points. Sensors derived from the specific wavelengths identified from growing peanut leaves, could also be developed and mounted on irrigation booms, tractors or as hand held devices so that real time crop assessments could be made while the crop is still in the ground. Specific aflatoxin and maturity indices derived from the wavelengths may also be applied to aerial hyperspectral imagery, providing a grower with the spatial information required to form more accurate harvesting regimes to obtain optimum product quality. The study has also shown that this may also be achieved through multispectral satellite and aerial IR imagery. The use of temporal images throughout the cropping season may also provide growers with the opportunity to monitor peanut crop performance with the potential to identify early incidences of stress, and hence the opportunity to implement remedial action to prevent more extensive crop damage. Similarly, the timing and effectiveness of crop irrigation could be better monitored through remote sensing by identifying deficiencies in the watering system, where regions of a crop suffering from an inadequate supply, would exhibit stress and therefore lower infrared reflectance. Finally, the adoption of multispectral imagery could allow the forecasting of total peanut cropping area, pod yield and hence total regional production which could benefit peanut marketers and planners.
... The further combination of the genetic concepts and field management are needed at each site to validate general recommendations [6]. Although, the greatest recorded yield for the peanut is 9.6 ton ha -1 , current commercial yields are 3 to 4 ton ha -1 in many countries and as low as 1 ton ha-1 in others [7]. Plant density and planting pattern are major cause of inability to achieve potential yield in irrigated and dry land production [8]. ...
... Furthermore, Madkour et al. [18] showed that the effects of row spacing on seed and pod yields were significant of 50cm row spacing, compared to 60cm row spacing. Planting peanut in the narrow twin row pattern did not increase peanut pod yield over the standard twin row planting pattern [7]. The objective of this study was to assess the further performance of the released varieties in comparison with other promise genotypes throughout its cultivation in different plant densities and its effects on yield, its components and quality. ...
Effect of genotype and plant density on growth characteristics and yield of Peanut (Archis hypogaea) in Iraq
... El patrón temporal lineal del IC ha sido discutido por muchos autores para un amplio rango de cultivos (Spaeth y Sinclair, 1985;Muchow, 1988Muchow, y 1990Hammer et al., 1995;Moot et al., 1996;Bange et al., 1998;Vega et al., 2000). Así, el uso del modelo lineal es lo suficientemente simple para usarse en los modelos de los cultivos (Hammer et al., 1995). ...
... El patrón temporal lineal del IC ha sido discutido por muchos autores para un amplio rango de cultivos (Spaeth y Sinclair, 1985;Muchow, 1988Muchow, y 1990Hammer et al., 1995;Moot et al., 1996;Bange et al., 1998;Vega et al., 2000). Así, el uso del modelo lineal es lo suficientemente simple para usarse en los modelos de los cultivos (Hammer et al., 1995). ...
To estimate the production of aerial biomass of crops using remote sensing, the production of biomass and spectral data in five crops measured in the field were analyzed during a sampling campaign in the Valle del Yaqui, Sonora. The spectral information was processed to obtain the vegetation iso-soil index (IVIS). Multitemporal analyses show a similar response between crop development and IVIS, and a linear relationship was obtained between them. Similarly, the harvest index and its relationship with IVIS were analyzed. The results showed that IVIS is a suitable index for estimating biomass and yield in crops.
... El patrón temporal lineal del IC ha sido discutido por muchos autores para un amplio rango de cultivos (Spaeth y Sinclair, 1985;Muchow, 1988Muchow, y 1990Hammer et al., 1995;Moot et al., 1996;Bange et al., 1998;Vega et al., 2000). Así, el uso del modelo lineal es lo suficientemente simple para usarse en los modelos de los cultivos (Hammer et al., 1995). ...
... El patrón temporal lineal del IC ha sido discutido por muchos autores para un amplio rango de cultivos (Spaeth y Sinclair, 1985;Muchow, 1988Muchow, y 1990Hammer et al., 1995;Moot et al., 1996;Bange et al., 1998;Vega et al., 2000). Así, el uso del modelo lineal es lo suficientemente simple para usarse en los modelos de los cultivos (Hammer et al., 1995). ...
RESUMEN Para estimar la producción de biomasa aérea en cultivos utilizando sensores remotos se realizaron análisis de la producción de biomasa e información espectral en cinco cultivos medidos en campo, durante una campaña de muestreo en el Valle del Yaqui, Sonora. La información espectral fue procesada hasta la obtención del índice de vegetación iso-suelo (IVIS). Los análisis multitemporales muestran un comportamiento similar entre el desarrollo de los cultivos y el valor del IVIS, por lo que se obtuvo una relación lineal entre ellos. De manera similar fueron analizados el índice de cosecha y su relación con el IVIS. Los resultados mostraron que el IVIS es un índice adecuado para la estimación de biomasa y rendimiento en cultivos. Palabras clave: biomasa, índice de cosecha, índice de vegetación, estimación de rendimientos. SUMMARY To estimate the production of aerial biomass of crops using remote sensing, the production of biomass and spectral data in five crops measured in the field were analyzed during a sampling campaign in the Valle del Yaqui, Sonora. The spectral information was processed to obtain the vegetation iso-soil index (IVIS). Multitemporal analyses show a similar response between crop development and IVIS, and a linear relationship was obtained between them. Similarly, the harvest index and its relationship with IVIS were analyzed. The results showed that IVIS is a suitable index for estimating biomass and yield in crops.
... So, the interdisciplinary nature of simulation modeling efforts leads to increased research efficacy and improved research direction through direct feedback. In this direction [22] developed BAsic CROp growth Simulator (BACROS) which was used as a reference model for developing other models and as a basis for developing summary models. Also [23] described the potential of simulation models in assessing trait benefits of winter cereals and their capacity to survive and reproduce in stress-prone environment. ...
... In addition, a functional equilibrium between root and shoot growth was added [30]. The introduction of micrometeorology in the models [31] and quantification of canopy resistance to gas exchanges allowed the models to improve the simulation of transpiration and evolve into the BAsic CROp growth Simulator (BACROS) [22]. ...
The Earth’s land resources are finite, whereas the number of people that the land must support increases rapidly, this situation has been a great concern in the area of agriculture. Crop production must be increased to meet the rapidly growing food demands through sophisticated agricultural processes, while it is important to protect other natural resources and the environment. New agricultural research is needed to provide additional information to farmers, policy makers and other decision makers on how to accomplish sustainable agriculture over the wide variations in climate change around the world. Therefore many researchers have over the years shown interest in finding ways to estimate the yield of crops before harvest. This paper reviews some of the crop growth models that have been successfully developed and used over time. The applications of crop growth models in agricultural meteorology, the role that climate changes play in these models and few of the successfully used crop models in agro-meteorology are also discussed in detail.
... Amissah-Arthur and Jagtap (1995) successfully assessed nitrogen requirements by maize across agroecological zones in Nigeria using CERES-maize model. Hammer et al. (1995) using local weather and soil information correlated peanut yields with estimates from PEANUTGRO, a model in the CERES family and gave a regression with high coefficient (r 2 = 0.93) of variation. Clifford et al. (2000) tested the effects of elevated CO2, drought and temperature on the water relations and gas exchange of groundnut. ...
... Amissah-Arthur and Jagtap (1995) successfully assessed nitrogen requirements by maize across agroecological zones in Nigeria using CERES-maize model. Hammer et al. (1995) using local weather and soil information correlated peanut yields with estimates from PEANUTGRO, a model in the CERES family and gave a regression with high coefficient (r 2 = 0.93) of variation. Clifford et al. (2000) tested the effects of elevated CO2, drought and temperature on the water relations and gas exchange of groundnut. ...
... Hammer et al. (1995);Moore et al. (1997);; Dolling et al. (2005); Peake, Whitbread, et al. (2008); Huth, Banabas, Nelson, and Webb (2014). Similarly, the data of the current study have been employed for a validity check of the wheat and sorghum modules in the APSIM model. ...
With the rapid global trend towards mechanized, continuous and dense cropping
systems that provide agricultural efficiency to meet consumer demand, soil
compaction has become a recognized problem. Soil compaction under modern
machines has had immense impact on productive land‘s physical, chemical and
biological properties, including soil-water storage capacity, fertiliser use efficiency,
and plant root architecture. As a result, farms are experiencing substantially reduced
crop yields and economic returns. The percentage of soil compaction increases with
increased soil clay fraction. Numerous investigations have been conducted to
evaluate the technical, economic and soil-crop efficiency of compaction mitigation
strategies, but deep tillage has not received sufficient consideration, particularly in
relation to high clay content soils.
This study was conducted to technically and economically evaluate a range of deep
ripping systems, and study the effect of tillage on soil and crop grown on cohesive
soils. A series of field experiments were conducted to parametrise a soil tillage force
prediction model, previously developed by Godwin and O‘Dogherty (2007) and the
Agricultural Productions Systems sIMulator (APSIM) developed by the Agricultural
Production Systems Research Unit in Australia (Holzworth et al., 2014; Keating et
al., 2003). The behaviour of soil physical properties, power requirements of ripping
operations and cost, and agronomic and economic performance of sorghum and
wheat were assessed at the University of Southern Queensland‘s research ground in
Toowoomba, Queensland (Australia) over two consecutive seasons (2015-16 and
2016-17). The work was conducted by replicating the soil conditions commonly
found in non-controlled or ‗random‘ traffic farming systems, referred to as RTF.
Sorghum was also grown at a commercial farm located in Evanslea near
Toowoomba, under controlled traffic (CTF) conditions (a farm system based on a
permanent lanes for machinery traffic) during the 2018 summer crop season.
The soil types at the two sites are Red Ferrosol (69.1% clay, 10.0% silt, and 20.9%
sand) and Black Vertosol (64.8% clay, 23.4% silt, and 11.8% sand). Three levels of
deep ripping depth, namely, Deep Ripping 1 (D1= 0-0.3 m), Deep Ripping 2 (D2= 0-
0.6 m), and Control (C= no ripping) were applied using a Barrow single tine ripper at
the Ag plot site - USQ, and a Tilco eight-tine ripper was used at the Evanslea site.
The tillage operations were performed at 2.7 km/h. A predetermined optimum N
fertiliser rate was applied after sorghum and wheat sowing at the Ag plot site. The
field experiments were conducted according to the randomized complete block
design (RCBD). The Statistical Package for Social Scientists (SPSS) software was
utilized to analyse the significance of the differences between the variables at the
probability level of 5% as the least significant difference (LSD).
The statistical analysis results showed that the D2 treatment significantly reduced
soil bulk density and soil strength by up to 5% and 24% for Red Ferrosol soil, and by
up to 6% and 40% for Black Vertosol soil respectively, and increased water content
compared with the D1 and C treatments. Overall results showed that D2 was superior in ameliorating the properties of both soils. In both soils, energy requirement results showed that tillage draft force and tractor power requirements were dependent on tillage depth, but for both tillage treatments, energy consumption was slightly lower for the CTF system (Evanslea site) than the RTF system at Ag plot site.
Crop performance results showed that at the Ag plot site, the grain and biomass
yields were highest by up to 19% for sorghum and by up to 30% for wheat when the
D2 treatment was applied, compared to the D1 and C treated crop yield components. Also, the grain and biomass yields were highest for fertilised soil by up to 10% for sorghum and by up to 16% and 25% for wheat respectively, in comparison with the non-fertilised treatments soils yield. Fertilising of D2 treated soil produced the highest significant yield of sorghum grain (5360 kg/ha), biomass (13269 kg/ha),
wheat grain (2419 kg/ha), and biomass (5960 kg/ha) compared to the yield of the
other treatment interactions. However, at Evanslea site, the D1 treatment showed
significantly higher yield and yield components for sorghum compared with C
practice (by up to 17% higher yield), and no differences were observed for treatment
D2. Economically, the D1 treatment required the lowest total operational cost at both
sites, which was estimated at AUD125/ha and AUD25.8/ha at the Ag plot and
Evanslea sites, respectively. These results compare to AUD139.3/ha (Ag plot) and
AUD30.8/ha (Evanslea) for the D2 ripping system. With regard to economic returns,
at the Ag plot site, D2 yielded the highest sorghum gross benefit (AUD1422/ha) and
net benefit (AUD1122/ha), wheat gross benefit (AUD590/ha) and net benefit (AUD482.3/ha), 2017 season gross benefit (AUD 2011.7/ha) and 2017 season net
benefit (AUD 1604.7/ha), compared to D1 and C soil benefits. The economic
fertiliser application at this site achieved the highest gross benefit for sorghum
(AUD1384.2/ha), wheat (AUD555.6/ha), and 2017 season (AUD1939.8/ha)
respectively, in comparison with the non-fertilised soils‘ total return. Also, fertilised
D2 treated soil resulted in the highest sorghum gross benefit (AUD1512.9/ha) and
net benefit (AUD1170.3/ha), wheat gross benefit (AUD633.7/ha) and net benefit
(AUD492.4/ha), 2017 season gross benefit (AUD2146.6/ha), and net benefit
(AUD1662.7/ha) compared to other interactions‘ benefits. At the Evanslea site, D1
significantly increased sorghum gross benefit and net benefit by up to 17%
(AUD2277.9/ha) and by up to 20% (AUD1825.5/ha), respectively compared to C
benefits, and no differences were observed with treatment D2.
The average of APSIM derived results for the long-term (1980-2017) at the Ag plot
site showed that the D2 treatment reported consistently higher grain sorghum (4192
kg/ha), biomass (11454 kg/ha), wheat grain (3783 kg/ha), and biomass (10623
kg/ha), compared to the D1 and C treatments‘ yields under the same long-term
conditions. However, at the Evanslea site, for long-term (1980-2018), APSIM
simulation showed that D1 treatment increased the yield of sorghum grain and
biomass significantly by up to 10% (5823 kg/ha) and 11% (12171 kg/ha),
respectively compared to C treatment‘s production, but these increases were found
not significant with the D2 yields‘ components. APSIM model simulation of field
experiment conditions during 2017 season at the Ag plot site showed that the D2
treatment also had the highest significant yield of sorghum grain (5284 kg/ha),
biomass (12488 kg/ha), wheat grain (2341 kg/ha) and biomass (6081 kg/ha)
compared to the C and D1 crop yields. Similarly, APSIM model simulation of field
experiment circumstances during the 2018 season at the Evanslea site showed that
the D1 treatment produced the highest yield of sorghum grain (7129 kg/ha), biomass
(13364 kg/ha) yields, compared to the C and D1 crop yields.
Overall, both the long and short-term model outputs were in good agreement with
experimental data, suggesting beneficial effects of deep tillage in improving cereal
crops‘ productivity in this region. Moreover, in comparison with the study findings,
the model prediction error rate was ±7, which indicates that the developed model
approach is valid and calibrated during this study.
Results derived from the G&O soil tillage mechanics model under the Ag plot and
Evanslea soil conditions showed that the required tractive force increases with the
increasing operation working depth. Furthermore, the D1 was superior, requiring the
lowest draft force at Ag plot (7.48 kN) and Evanslea (19.65 kN) soils, compared to
the D2 required forces which were 43.28 kN and 41.41kN at both sites, respectively.
In general, the model values were in line with the experiments' draft forces and when compared with the study readings, the model prediction error rate was ±8, which indicates that it is also valid and calibrated during this study.
Finally, the study provides conclusions and recommendations that contribute to crop production improvement in the face of recurrent and increasing challenges, as well as emphasizing the necessity of correct management and cultivation of economically important crops after the application of deep ripping to produce accurate results that serve decision-making in the agricultural sector.
... APSIM was a process-based crop model that could better capture the interaction between environmental factors and crop production. The APSIM-Peanut module used in this study was built upon an original peanut simulation model developed by Hammer et al. (1995) with large improvements. Details about the module were described in Robertson et al. (2002), which evaluated the performance of APSIM in simulating the growth and development for several leguminous crops. ...
Developing a reasonable crop planting layout under climate change is critical for improving peanut yield and alleviating the contradiction of edible oil in China. Henan province is strengthening summer peanut production, and the summer peanut climatic suitability was evaluated from 1981 to 2019 at county level by coupling drought-waterlogging risk with potential productivity. Two yield levels, i.e., yield potentials under well-watered conditions (Yp) and under rainfed conditions (Yrp) simulated by Agricultural Production Systems sIMulator (APSIM-Peanut), were considered to indicate the potential productivity under different growing environments. Higher Yp (or Yrp) mainly located in the northern (or southern) parts of Henan province. Differences in the distribution of Yp and Yrp caused the divergent patterns in the climatic suitability under these two conditions. The suitable zones under well-watered conditions were mainly located in the western and central parts, whereas the suitable zones under rainfed conditions were mainly located in the southern parts. Divergent patterns were detected in other parts, where climatic suitability under well-watered relative to rainfed conditions changed from suitable zones to near-suitable zones in western parts and from near-suitable to non-suitable zones in northern parts, indicating that irrigation could improve climatic suitability for summer peanut planting in these regions. A total of 56% of the peanut planting area was matched with the suitable zones under rainfed conditions based on the county-level peanut planting area from 2013 to 2017. Collectively, our work provides a novel framework for estimating crop planting suitability, and can offer a refined benchmark for summer peanut planting layout.
... Simple Simulation Models (SSM) are a group of crop models that date back to 1986 when a simple simulation model was developed for soybean (Sinclair, 1986). The framework has been improved and applied over the past 30 years to nearly all major grain crops including maize (Sinclair and Muchow, 1995), sorghum (Sinclair et al., 1997), wheat (Sinclair and Amir, 1992;Soltani et al., 2013), barley (Wahbi and Sinclair, 2005), peanut (Hammer et al., 1995), and chickpea (Soltani and Sinclair, 2011). A complete description of SSM can be found in Soltani and Sinclair (2012) and Soltani et al. (2013). ...
Crop models are essential in undertaking large scale estimation of crop production of diverse crop species, especially in assessing food availability and climate change impacts. In this study, an existing model (SSM, Simple Simulation Models) was adapted to simulate a large number of plant species including orchard species and perennial forages. Simplification of some methods employed in the original model was necessary to deal with limited data availability for some of the plant species to be simulated. The model requires limited, readily available input information. The simulations account for plant phenology, leaf area development and senes-cence, dry matter accumulation, yield formation, and soil water balance in a daily time step. Parameterization of the model for new crops/cultivars is easy and straightforward. The resultant model (SSM-iCrop2) was para-meterized and tested for more than 30 crop species of Iran using numerous field experiments. Tests showed the model was robust in the predictions of crop yield and water use. Root mean square of error as percentage of observed mean for yield was 18% for grain field crops, 14% for non-grain crops 14% for vegetables and 28% for fruit trees.
... The radiation use efficiency declines with suboptimal or supra-optimal average daily temperature (Bell et al., 1992;Wright et al., 1993). Biomass partitioning is driven by a daily rate increment on harvest index up to a genotype-specific maximum (Hammer et al., 1995). ...
In the semiarid pampas region of Argentina, peanut (Arachis hypogaea L.) crop undergoes frequent and unpredictable drought stress periods, with high probability of occurrence under future climate change projections. However, the overall frequency of occurrence and timing of different drought stress patterns under current and future climates has not been well investigated for the main peanut region in central Argentina. The aims of this study were to: (i) define the main peanut drought stress patterns and their occurrence (frequency) during the peanut growing season, via a crop growth modeling approach, (ii) test seed yield stability of the formerly defined (in i) drought stress patterns under future climate scenarios, and (iii) analyze the effect of sowing dates on peanut seed yield as a management strategy to mitigate the impact of future climate scenarios for peanut seed yield in the semiarid pampas region of Argentina. The APSIM-peanut growth model was calibrated and validated for local genotypes and production environments. Our study simulated seasonal drought stress patterns at five representative locations across the region under current and future climate scenarios, and tested six sowing dates as a practice to mitigate the impact of climate change. Clustering analysis identified two contrasting environment types (ETs). The high stress ET showed greater frequency of occurrence (>50%) at the southern locations. Future climate conditions increased the frequency of high stress ET by roughly 6% and reduced peanut seed yield by 12%, as average across locations. For both current and future climates, earlier sowing dates maximized peanut seed yield, regardless the locations. Changing sowing date was not an effective practice to mitigate the negative impact of climate change on peanut seed yield. The defined ETs allowed identifying a target population of environments (TPE) having implications for optimizing peanut breeding and management strategies. The projected increase in the frequency of drought stress for all tested locations provides a challenging scenario for sustaining peanut productivity in this region.
... Simple Simulation Models (SSM) are a group of crop models that date back to 1986 when a simple simulation model was developed for soybean (Sinclair, 1986). The framework has been improved and applied over the past 30 years to nearly all major grain crops including maize (Sinclair and Muchow, 1995), sorghum (Sinclair et al., 1997), wheat (Sinclair and Amir, 1992;Soltani et al., 2013), barley (Wahbi and Sinclair, 2005), peanut (Hammer et al., 1995), and chickpea (Soltani and Sinclair, 2011). A complete description of SSM can be found in Soltani and Sinclair (2012) and Soltani et al. (2013). ...
Crop models are essential in undertaking large scale estimation of crop production of diverse crop species, especially in assessing food availability and climate change impacts. In this study, an existing model (SSM, Simple Simulation Models) was adapted to simulate a large number of plant species including orchard species and perennial forages. Simplification of some methods employed in the original model was necessary to deal with limited data availability for some of the plant species to be simulated. The model requires limited, readily available input information. The simulations account for plant phenology, leaf area development and senes-cence, dry matter accumulation, yield formation, and soil water balance in a daily time step. Parameterization of the model for new crops/cultivars is easy and straightforward. The resultant model (SSM-iCrop2) was para-meterized and tested for more than 30 crop species of Iran using numerous field experiments. Tests showed the model was robust in the predictions of crop yield and water use. Root mean square of error as percentage of observed mean for yield was 18% for grain field crops, 14% for non-grain crops 14% for vegetables and 28% for fruit trees.
... SSM has been used to examine yield potential and production risks for a range of crop species including spring wheat (Triticum aestivum L.) (Amir & Sinclair, 1991), maize (Zea mays L.) (Muchow & Sinclair, 1991), sorghum (Sorghum bicolor L. moench) (Hammer & Muchow, 1994), and grain legumes such as cowpea (Vigna unguiculata (L.) Walp.) and black-gram (Vigna mungo (L.) Hepper) (Sinclair et al., 1987), peanut (Arachis hypogaea) (Hammer et al., 1995), chickpea (Cicer arietinum L.) (Soltani et al., 1999), and lentil (Lens culinaris, L.) (Ghanem et al., 2015). SSM has now also been parameterized for faba bean and shown to be robust in simulating crop development, growth, and yield (Marrou et al., 2021). ...
Faba bean (Vicia faba L.) is a useful grain legume for production in Mediterranean climates due to its consumption as food for humans and feed for animals, and its ability to symbiotically fix atmospheric nitrogen. Currently, in Morocco a substantial fraction of faba bean is sown under a rainfed management scheme in which the crop is sown after about a 15-day delay following the first rains after the dry season. The 15-day delay allows weed seeds to germinate and be killed during land tillage prior to sowing of faba bean. However, the 15-day delay shortens the growing season and may negatively impact seed yield. Two alternate sowing date criteria were simulated for faba bean sowing date in Morocco as approaches to increase production. In addition to the 15-day delay management by farmers, sowing was simulated to occur immediately following accumulation of 10 mm or 25 mm of water in the soil. A geospatial analysis was undertaken using the SSM-faba bean model to simulate production on a 1° × 1° grid across Morocco. Eighty three locations were each simulated for 30 growing seasons of weather input. The simulation results for the 25-mm sowing date criteria resulted in decreased geographical area in which faba bean could be grown while the 10-mm sowing date criteria resulted in an expanded geographical area for faba bean production. The average yield based only on seasons in which sowing was achieved, was fairly stable among the sowing-date criteria. The probability of yield increase of the 10-mm sowing date criterion as compared to the 15-day delay sowing was greater than 50% in much of the area found suitable for faba bean production. Assuming an acceptable method for weed control for the 10-mm sowing date criterion, this alternate management could expand faba bean production in Morocco as compared to the current practice of a 15-delay in sowing date.
... Simple Simulation Models (SSM) are a group of crop models that date back to 1986 when a simple simulation model was developed for soybean (Sinclair, 1986). The framework has been improved and applied over the past 30 years to nearly all major grain crops including maize (Sinclair and Muchow, 1995), sorghum (Sinclair et al., 1997), wheat (Sinclair and Amir, 1992;Soltani et al., 2013), barley (Wahbi and Sinclair, 2005), peanut (Hammer et al., 1995), and chickpea (Soltani and Sinclair, 2011). A complete description of SSM can be found in Soltani and Sinclair (2012) and Soltani et al. (2013). ...
... Keberhasilan polinasi dipengaruhi oleh faktor genetik yaitu banyaknya jumlah serbuk sari yang menempel pada permukaan stigma (Marcucci dan Visser, 1987;Sutapradja, 2008). Faktor lingkungan seperti iklim, tanah, teknik bercocok tanam (Hammer et. al., 1995;Sutapradja, 2008), tingkat serangan hama dan penyakit juga menjadi pembatas keberhasilan persilangan. Selain itu, pemilihan bunga yang kurang tepat, cara emaskulasi yang salah dan saat menyentuhkan polen ke putik yang kurang hati-hati, juga dapat mengakibatkan polinasi tidak berhasil. ...
... CERESpearl millet model, CROPSYST, PmModels are being used to study the suitability and yield simulation of pearl millet genotypes across the globe. Hammer et al. (1995) using local weather and soil information correlated peanut yields with estimates from PEANUTGRO, a model in the CERES family and gave a regression with high coefficient (r 2 = 0.93) of variation. The construction of contemporary crop models entails the combination of many algorithms for physiological processes and impact of environmental factors on process rates (Monteith, 2000). ...
Agriculture continues to be the backbone of Third World economies. In India, more than two-thirds of population depends on agriculture. Agriculture provides the principal means of livelihood for over 58.4% of India's population. So the promotion of agriculture is an integral part of developmental programmes. The advances through information technology and space technology need to be extended to agriculture as well. Agriculture is always vulnerable, because of unfavorable weather and climatic conditions. So, it needs constant monitoring to improve crop productivity. The linkages among crop varieties, irrigation, soil characteristics, weather, etc., which are the key factors in agricultural productivity can be effectively made with the help of Remote Sensing and GIS tools. Crop Simulation Model (CSM) is a valuable tool to researchers to help them to understand the influence of climatic variables on crop productivity. Simulation models also provide global edge to the farmers and researchers since they are objective, fast and cost effective. The scope of applicability of these simulation models can be extended too much broader scales for regional planning and policy analysis by combining their capabilities with a Geographic Information System (GIS). The principle of this study appeal to review on Remote Sensing (RS), GIS and CSM, and on types of models and its limitations. Overview of CSM models in the current scenario, as well utilization of RS and GIS tools that model the impacts of agricultural interventions.
... قرار کرد ن د پراکند میزان قت گ داده ی دقت میزان و مدل های داده برآورد تغییر ضریب از ها پذیری با برابر که 97 / 4 درصد است استفاده شد خطا مربعات میانگین جذر . یعنی RMSE با برابر 77 / 5 به نسبت که بود دیگر ک صفات ه مدل با پ ی ش ب ی ن ی شده اند دقت به نسبت و داشته کمتری همان از که گونه بر تبیین ضریب م ی آ ی د ( 67 / 0 ،) ذرت ( Sinclair & Muchow, 1999;Turabi & Soltani, 2013 ) سورگوم ، ( Hammer & Muchow, 1994 ) بادام ، زمینی ( Hammer et al., 1995 ) نخود و ( Soltani et al., 1999 ...
In order to modeling of growth stages and yield of corn according to Hamedan province meteorological data (minimum and maximum temperature, radiation and rainfall) By using the sub models of phenology, production and distribution of dry matter and leaf area changes in maize studies was conducted at the Faculty of Agriculture, University of Vali-e-Asr Rafsanjan in spring 2015. Daily changes of phenology, total dry matter and leaf area was calculated using the model and the yield was predicted. One of the criteria to evaluation of a model is Comparison between coefficients of linear regression of observed and predicted yield (b=0.29+- 2.11 and a=0.93+- 0.23) and coefficients of line 1:1 (1, 0). Accuracy of the model related to coefficient of variations of predicted and observed seed yield (CV= 4.13) was very high so that in field experiments coefficient of variations limit is 20 to 25. R2 quantity of seed yield was 0.69; showing that the probability for coordination of predicted and observed data is 69 percent. The Root mean square error is the other statistics which is used to evaluation of model accuracy. The Root mean square error of seed yield was 0.36, which is evidence of accuracy of model for yield prediction. domain variation for observed and predicted data were 8.54-9.99 tones and 8.02-9.25 tons per hectare respectively and the means were 9.09 and 8.75 tones per hectare respectively.
Keywords: Modeling, Phenology, Grain yield, Corn
... The model was set to simulate a representative Spanish cultivar and the root mean square of error (RMSE) of the relationship between the observed and the simulated pod yield was 16% of the average observed pod yield, indicating robustness of the model to simulate pod yield across a range of water regimes and across varied environments (Vadez et al., 2016). This model is similar in many ways to a previously developed groundnut model and also successfully tested in Australia (Hammer et al., 1995). A validation of the model's simulation of the leaf canopy development has been recently reported (Halilou et al., 2016). ...
Groundnut production is limited in Sub-Saharan Africa and water deficit or “drought,” is often considered as the main yield-limiting factor. However, no comprehensive study has assessed the extent and intensity of “drought”-related yield decreases, nor has it explored avenues to enhance productivity. Hence, crop simulation modeling with SSM (Simple Simulation Modeling) was used to address these issues. To palliate the lack of reliable weather data as input to the model, the validity of weather data generated by Marksim, a weather generator, was tested. Marksim provided good weather representation across a large gradient of rainfall, representative of the region, and although rainfall generated by Marksim was above observations, run-off from Marksim data was also higher, and consequently simulations using observed or Marksim weather agreed closely across this gradient of weather conditions (root mean square of error = 99 g m⁻²; R² = 0.81 for pod yield). More importantly, simulation of yield changes upon agronomic or genetic alterations in the model were equally predicted with Marksim weather. A 1° × 1° grid of weather data was generated. “Drought”-related yield reduction were limited to latitudes above 12–13° North in West Central Africa (WCA) and to the Eastern fringes of Tanzania and Mozambique in East South Africa (ESA). Simulation and experimental trials also showed that doubling the sowing density of Spanish cultivars from 20 to 40 plants m⁻² would increase yield dramatically in both WCA and ESA. However, increasing density would require growers to invest in more seeds and likely additional labor. If these trade-offs cannot be alleviated, genetic improvement would then need to re-focus on a plant type that is adapted to the current low sowing density, like a runner rather than a bush plant type, which currently receives most of the genetic attention. Genetic improvement targeting “drought” adaptation should also be restricted to areas where water is indeed an issue, i.e., above 12–13°N latitude in WCA and the Eastern fringes of Tanzania and Mozambique.
... In crop simulations, total plant leaf area (PLA) is then calculated as an empirical function of main stem node number. The crop model APSIM first estimates the effective leaf number for the entire plant based on main stem node number (Hammer et al., 1995;Robertson et al., 2002). The specific steps in this approach are calculation of: (1) node number on main-stem based on cumulative temperature, (2) total plant leaf number from main stem node number, (3) fraction of senesced leaf number on main stem based on cumulative temperature, (4) plant senesced leaf number from main stem senesced leaf number, (5) green leaf number from total and senesced leaves, (6) individual leaf size from main stem node number or cumulative temperature (assumed to be constant 40 cm 2 in peanut), (7) PLA as the product of total leaf number per plant and individual leaf size, (8) leaf area index (LAI) from plant leaf area and plant density. ...
Rapid leaf area development may be attractive under a number of cropping conditions to enhance the vigor of crop establishment and allow rapid canopy closure for maximizing light interception and shading of weed competitors. This study was undertaken to determine (1) if parameters describing leaf area development varied among ten peanut (Arachis hypogeae L.) genotypes grown in field and pot experiments, (2) if these parameters were affected by the planting density, and (3) if these parameters varied between Spanish and Virginia genotypes. Leaf area development was described by two steps: prediction of main stem number of nodes based on phyllochron development and plant leaf area dependent based on main stem node number. There was no genetic variation in the phyllochron measured in the field. However, the phyllochron was much longer for plants grown in pots as compared to the field-grown plants. These results indicated a negative aspect of growing peanut plants in the pots used in this experiment. In contrast to phyllochron, there was no difference in the relationship between plant leaf area and main stem node number between the pot and field experiments. However, there was genetic variation in both the pot and field experiments in the exponential coefficient (PLAPOW) of the power function used to describe leaf area development from node number. This genetic variation was confirmed in another experiment with a larger number of genotypes, although possible G×E interaction for the PLAPOW was found. Sowing density did not affect the power function relating leaf area to main stem node number. There was also no difference in the power function coefficient between Spanish and Virginia genotypes. SSM (Simple Simulation model) reliably predicted leaf canopy development in groundnut. Indeed the leaf area showed a close agreement between predicted and observed values up to 60000cm2m−2. The slightly higher prediction in India and slightly lower prediction in Niger reflected GxE interactions. Until more understanding is obtained on the possible GxE interaction effects on the canopy development, a generic PLAPOW value of 2.71, no correction for sowing density, and a phyllochron on 53°C could be used to model canopy development in peanut.
... This parameter is related to interception of radiation, photosynthesis, biomass accumulation, transpiration and gas exchange in crop canopies (Kucharik et al. 1998). It is also one of the most relevant parameters in experimentation, and has been used to predict harvest date (Hammer et al. 1995;Kiniry et al. 1996). Variables that are useful in agriculture and other disciplines, such as the leaf area index (LAI), which is defined as the total one-sided area of leaf tissue per unit ground surface area (Watson, 1947) are also computed from the leaf area. ...
Leaf area is one of the most important parameters for characterizing crop growth and development, and its measurement is useful for examining the effects of agronomic management on crop production. It is related to interception of radiation, photosynthesis, biomass accumulation, transpiration and gas exchange in crop canopies. Several direct and indirect methods have been developed for determining leaf area. The aim of this study is to develop an indirect method, based on the use of a mathematical model, to compute leaf area in an onion crop using non-destructive measurements with the condition that the model must be practical and useful as a Decision Support System tool to improve crop management. A field experiment was conducted in a 4.75 ha commercial onion plot irrigated with a centre pivot system in Aguas Nuevas (Albacete, Spain), during the 2010 irrigation season. To determine onion crop leaf area in the laboratory, the crop was sampled on four occasions between 15 June and 15 September. At each sampling event, eight experimental plots of 1 m2 were used and the leaf area for individual leaves was computed using two indirect methods, one based on the use of an automated infrared imaging system, LI-COR-3100C, and the other using a digital scanner EPSON GT-8000, obtaining several images that were processed using Image J v 1.43 software. A total of 1146 leaves were used. Before measuring the leaf area, 25 parameters related to leaf length and width were determined for each leaf. The combined application of principal components analysis and cluster analysis for grouping leaf parameters was used to reduce the number of variables from 25 to 12. The parameter derived from the product of the total leaf length (L) and the leaf diameter at a distance of 25% of the total leaf length (A25) gave the best results for estimating leaf area using a simple linear regression model. The model obtained was useful for computing leaf area using a non-destructive method.
... Application of crop simulation models in combination with long-term climate data can allow an assessment of the peanut production potential, appropriate management and variety options and possible climatic constraints for productivity and quality for a given environment. The peanut module of the Agricultural Production Systems Simulator (APSIM) developed by the Agricultural Production Systems Research Unit can simulate crop growth and yield using daily weather (Hammer et al. 1995; Robertson et al. 2002) and is being extensively used to predict potential yields and afl atoxin risk in various production systems (Wright et al. 2005). The APSIM peanut model has, however, not been previously applied to simulate peanut crops in PNG environments. ...
... As shown in Fig. 1, soil-water content in the I 100 plots during the aforementioned period were sufficient to meet the crop requirements. Hammer et al. (1995) stated that peanut yield may be regulated by the amount of water available to the plant during its development, and Ramamoorthy and Basu (1996) reported that occurrence of water stress during flowering stage affects the number of mature pods per plant and the seed size. Soil-water depletion increased with decreasing amount of irrigation. ...
This study was carried out to determine the effect of regular deficit drip irrigation strategies on growth, yield, and yield
components, as well as water use efficiency (WUE) under Mediterranean conditions. Peanut (Arachis hypogaea cv. NC-7) crops were grown
in the seasons of 2013 and 2014. Irrigation water applications were 0 (I0), 25% (I25), 50% (I50), 75% (I75), 100% (I100), and 125% (I125)
based on cumulative evaporation (Epan) measured in a Class A pan. Different irrigation levels applied have statistically significant effects on
yield components such as plant height, primary branch length and number, dry shoot and root weight, number of pods, and 100-seed weight.
In both years, water stress significantly decreased linoleic acid, protein, and oil content, although it increased oleic acid. The I100 irrigation
treatment produced the highest protein value (32.5% in 2013 and 32.7% in 2014), whereas I0 yielded the lowest values (24.6% in 2013 and
25.9% in 2014). The maximum seed yield was obtained from I100 treatment in both years (5.25 t ha−1 in 2013 and 5.36 t ha−1 in 2014).
Compared to I100, the two-year average seed yield reduction for I0, I25, I50, I75, and I125 were 81.0, 68.5, 28.5, 12.0, and 4.5%, respectively.
The highest WUE was obtained from I50 and from I75 treatment in the first and second year, respectively, as much as 7.5 kg ha−1 mm−1.
Based on the combined effects of yield reduction, WUE, and seed quality characteristics, peanuts can be irrigated as much as 100% of pan
evaporation when water shortage is not a concern, and it can be irrigated as much as 75% of pan evaporation under water shortage conditions.
... A fraction of stem and pod wall mass (0.25 and 0.30, respectively), at the start of grain filling is deemed potentially available for grain filling (Diepenbrock 2000), if demand exceeds the supply of assimilate from current photosynthesis. The demand for assimilate for grain yield accumulation is defined by the linear increase with time of the harvest index, first noted as a phenomenon in soybean (Spaeth and Sinclair 1985) and since then described in many species including peanut (Hammer et al. 1995), maize and sorghum (Muchow 1988), pigeonpea and wheat (Moot et al. 1996). Such a simple approach avoids the complex interactions and trade-offs between grain yield components as summarised by Diepenbrock (2000). ...
The canola (Brassica napus L.) module in the Agricultural Production Systems Simulator (APSIM) was developed in the late 1990s. There has been no peer-reviewed account of the scientific underpinnings of the module, despite considerable testing across a wide range of environments in the Australian grains industry and numerous applications of the model to address agronomic and crop adaptation issues. This paper presents a summary of the parameters in the module and reviews the physiological evidence justifying their values and module performance, and reflects on areas of module improvement and application. APSIM-Canola simulates crop development, growth, yield and nitrogen (N) accumulation in response to temperature, photoperiod, radiation, soil water and N supply, with a daily time-step, using well-accepted approaches. The module has been validated on more than 250 data points across Australia, China, and Germany and typical root mean squared deviations for days to flowering are ∼5 days and for grain yield are ∼0.4tha-1. Testing on vernalisation-responsive winter types and in high yielding situations has indicated that more research is required to define phenology parameters and yield forming processes in high yielding environments. There is a need to develop better predictive routines for grain oil content that take account of the dynamics of grain filling and interactions with environmental conditions, and improve upon current regression-type approaches. Further testing of N responses is required. Physiological characterisation of new cultivar types, such as hybrids, Indian mustard (Brassica juncea), and new herbicide tolerance types is required to make the module more applicable to contemporary canola production systems. A lack of understanding of the effects of high and low temperature extremes on reproductive processes is currently limiting the use of the module outside conventional sowing dates and agro-climatic zones.
... Neither individual seed growth rate nor effective seed-filling duration was influenced by elevated CO 2 . Exposure to high temperatures has previously been reported to decrease both seed harvest index and rate of change of harvest index in peanut (Hammer et al., 1995;Craufurd et al., 2002). This was re-confirmed in our study, which showed the full range of decline in seed harvest index from 32/22 to 44/34 1C, projecting to zero at 45/35 1C. ...
Continuing increases in atmospheric carbon dioxide concentration (CO2) will likely be accompanied by global warming. Our research objectives were (a) to determine the effects of season-long exposure to daytime maximum/nighttime minimum temperatures of 32/22, 36/26, 40/30 and 44/34degreesC at ambient (350 mumol mol(-1)) and elevated (700 mumol mol(-1)) CO2 on reproductive processes and yield of peanut, and (b) to evaluate whether the higher photosynthetic rates and vegetative growth at elevated CO2 will negate the detrimental effects of high temperature on reproductive processes and yield. Doubling of CO2 increased leaf photosynthesis and seed yield by 27% and 30%, respectively, averaged across all temperatures. There were no effects of elevated CO2 on pollen viability, seed-set, seed number per pod, seed size, harvest index or shelling percentage. At ambient CO2, seed yield decreased progressively by 14%, 59% and 90% as temperature increased from 32/22 to 36/26, 40/30 and 44/34degreesC, respectively. Similar percentage decreases in seed yield occurred at temperatures above 32/22degreesC at elevated CO2 despite greater photosynthesis and vegetative growth. Decreased seed yields at high temperature were a result of lower seed-set due to poor pollen viability, and smaller seed size due to decreased seed growth rates and decreased shelling percentages. Seed harvest index decreased from 0.41 to 0.05 as temperature increased from 32/22 to 44/34degreesC under both ambient and elevated CO2. We conclude that there are no beneficial interactions between elevated CO2 and temperature, and that seed yield of peanut will decrease under future warmer climates, particularly in regions where present temperatures are near or above optimum.
... This parameter is related to interception of radiation, photosynthesis, biomass accumulation, transpiration and gas exchange in crop canopies (Kucharik et al. 1998). It is also one of the most relevant parameters in experimentation, and has been used to predict harvest date (Hammer et al. 1995;Kiniry et al. 1996). Variables that are useful in agriculture and other disciplines, such as the leaf area index (LAI), which is defined as the total one-sided area of leaf tissue per unit ground surface area (Watson, 1947) are also computed from the leaf area. ...
Leaf area is one of the most important parameters for characterizing crop growth and development, and its measurement is useful for examining the effects of agronomic management on crop production. It is related to interception of radiation, photosynthesis, biomass accumulation, transpiration and gas exchange in crop canopies. Several direct and indirect methods have been developed for determining leaf area. The aim of this study is to develop an indirect method, based on the use of a mathematical model, to compute leaf area in an onion crop using non-destructive measurements with the condition that the model must be practical and useful as a Decision Support System tool to improve crop management. A field experiment was conducted in a 4.75 ha commercial onion plot irrigated with a centre pivot system in Aguas Nuevas (Albacete, Spain), during the 2010 irrigation season. To determine onion crop leaf area in the laboratory, the crop was sampled on four occasions between 15 June and 15 September. At each sampling event, eight experimental plots of 1 m
... The plant modules simulate key underpinning physiological processes and operate on a daily time step in response to input daily weather data, soil characteristics and crop management actions. The crop modules have evolved from early versions for focus crops such as maize (Carberry and Abrecht, 1991), peanut (Hammer et al., 1995), sorghum (Hammer and Muchow, 1991) and sunflower (Chapman et al., 1993). The initial crop modules of APSIM utilised concepts from existing models available at the time (e.g. ...
... There are a number of approaches to simulating the growth of reproductive organs. The simplest models do not distinguish among yield components, fertile plants and tillers, fruit and seed number and weight; all components are lumped together in a coeffi cient (harvest index) that describes the fraction of total growth that corresponds to reproductive structures ( Figure 4a ; Sinclair, 1986 ;Muchow et al., 1990 ;Hammer et al., 1995). This approach is robust for the simulation of variation in yield across a range of environments but lacks suffi cient detail to describe genotypic effects such as differences in maturity in sorghum ( Hammer and Broad, 2003 ), as well as other genotypic effects underpinning yield improvement and tolerance to stresses. ...
This chapter introduces fundamental concepts of modeling natural systems using a G×E×M framework and discusses the prospects for an integrated approach for improving crop performance that tackles the G×E×M interactions holistically. It outlines general principles of modeling biophysical systems, including a description of fundamental components of crop models. It describes G×E×M systems and introduces gene-to-phenotype (GP) models and concepts of adaptation landscapes as applied to plant breeding. The chapter discusses the application of the framework by studying genetic improvement of maize in the US Corn Belt. It reviews and summarizes theoretical developments toward a framework that integrates quantitative genetics, breeding simulation, and modeling of physiological traits and dynamic GP relations. Such a framework is intended to enable breeders and agronomists to project trajectories in the G×E×M space into the future and gain insights on the consequences of manipulating genomes to the creation of improved crops for target management and environments.
... Wide variations in peanut RUE are reported in the literature under various agro-ecological conditions (Kiniry et al., 2005). Variations in peanut and other crops' RUE have been modeled based on night temperature (Hammer et al., 1995), vapor pressure deficit (Kiniry et al., 1992) or radiation (Stockle and Kemanian, 2009). Further model improvement will involve modifications to the crop RUE to account for differences in vapor pressure deficit and non-optimum temperature. ...
... Amissah-Arthur and Jagtap (1995) successfully assessed nitrogen requirements by maize across agroecological zones in Nigeria using CERES-maize model. Hammer et al. (1995) using local weather and soil information correlated peanut yields with estimates from PEANUTGRO, a model in the CERES family and gave a regression with high coefficient (r 2 = 0.93) of variation. Clifford et al (2000) tested the effects of elevated CO 2 , drought and temperature on the water relations and gas exchange of groundnut. ...
The Earth's land resources are finite, whereas the number of people that the land must support continues to grow rapidly. This creates a major problem for agriculture. Production (productivity) must be increased to meet rapidly growing demands while natural resources must be protected. New agricultural research is needed to supply information to farmers, policy makers and other decision makers on how to accomplish sustainable agriculture over the wide variations in climate around the world. In this direction the use of crop models in research is being encouraged.
An approach based on a linear rate of increase in harvest index (HI) with time after anthesis has been used as a simple means to predict grain growth and yield in many crop simulation models. When applied to diverse situations, however, this approach has been found to introduce significant error in grain yield predictions. Accordingly, this study was undertaken to examine the stability of the HI approach for yield prediction in sorghum [ Sorghum bicolor (L.) Moench]. Four field experiments were conducted under nonlimiting water and N conditions. The experiments were sown at times that ensured a broad range in temperature and radiation conditions. Treatments consisted of two population densities and three genotypes varying in maturity. Frequent sequential harvests were used to monitor crop growth, yield, and the dynamics of HI. Experiments varied greatly in yield and final HI. There was also a tendency for lower HI with later maturity. Harvest index dynamics also varied among experiments and, to a lesser extent, among treatments within experiments. The variation was associated mostly with the linear rate of increase in HI and timing of cessation of that increase. The average rate of HI increase was 0.0198 d ⁻¹ , but this was reduced considerably (0.0147) in one experiment that matured in cool conditions. The variations found in HI dynamics could be largely explained by differences in assimilation during grain filling and remobilization of preanthesis assimilate. We concluded that this level of variation in HI dynamics limited the general applicability of the HI approach in yield prediction and suggested a potential alternative for testing.
Introduction:
Crop simulation models are very useful tools for evaluation plant growth and development processes. Crop simulating models may be used to estimate yield and evaluate climatic, plant and management parameters on yield. Also, it may be used to predict crop water requirement under different conditions. Crop models should be evaluated and parameterized to simulate crop growth and development. Parameterization is used for precise simulation of crop growth and development and can estimate the best and most appropriate values for model parameters obtained via observed data or calibration. The objectives of this study were to describe SSM-iCrop2 model, determine plant parameters and evaluation of alfalfa in its major production regions using SSM-iCrop2 model in Iran.
Materials and Methods:
SSM-iCrop2 crop simulation model is a simplified form of SSM crop models which is suitable for simulation of growth, development and yield of different crops under different environmental conditions and large-scale estimation of crop production, especially in evaluation of nutrient availability and climatic effects. This model includes sub models of phenology, leaf expansion and senescence, dry matter production and distribution and soil water balance. Daily weather data, agronomic management, soil properties and plant parameters are required for simulation in this model. The present study investigates the performance of SSM-iCrop2 model regarding the prediction of single cuts and overall cuts, phonologic stages and water requirement of alfalfa under changing climatic conditions of Iran. To simulate the growth, development and yield of alfalfa using SSM-iCrop2 model in Iran, the major irrigated alfalfa production provinces including East Azarbaijan, Hamedan, West Azarbaijan, Sistan and Baluchestan, Khorasan Razavi, Esfahan, Kordestan, Ghazvin, Ardabil, Markazi, Fars, Zanjan, Chaharmahal and Bakhtiyari and Tehran were identified based on the data available in Ministry of Agriculture statistics. Then, field experiment data required for model parameterization and estimation were collected from these provinces.
Results and Discussion:
According to the results of SSM-iCrop2 model parameterization, two cultivars with different leaf area indices (high-yielding and low-yielding) were determined in major alfalfa production provinces. The model was evaluated using the independent experimental data which had not been used for parameterization. The results of evaluation for alfalfa yield showed that the range of the observed single-cut forage yield was between 112 to 640 g m-2 with an average of 330 g m-2; the observed total annual forage yield ranged from 646 to 4042 g m-2 with an average of 1717 g m-2; and water requirement of alfalfa obtained from NETWAT software was between 5140 to 12690 m3 ha-1 with an average of 8746 m3 ha-1. The range of the predicted single-cut forage yield, the predicted total annual forage yield, and alfalfa water requirement were obtained 189 to 457 g m-2 with an average of 351 g m-2, 693 to 3296 g m-2 with an average of 1654 g m-2 and 4093 to 16874 m3 ha-1 with an average of 10940 m3 ha-1. Overall, in the evaluation for observed versus simulated alfalfa forage yield, 31 points were obtained for single-cut yield with the correlation coefficient (r) 0.79, root mean square error (RMSE) 88.3 g m-2, and coefficient of variation (CV) 26.78%, respectively, and 21 points were obtained for annual yield with 0.90 for r, 344.4 g m-2 for RMSE, and 20.05% for CV, respectively. Besides, the evaluation results indicated that r, RMSE, and CV for observed versus simulated alfalfa water requirement were 0.43, 3503 m3 ha-1, and 40%, respectively.
Conclusion:
The results obtained from parameterization and evaluation of SSM-iCrop2 model show that the mentioned model presents a logical prediction and accurate estimation of model parameters for yield and water requirement of alfalfa crop in Iran. Thus, this model may be used for prediction of alfalfa yield under different climates and management conditions.
Introduction
Chickpea yield is at low levels in major producing countries, indicating needs to increase crop yield via crop genetic improvement and enhanced crop management. Genetic and management constraints can be analyzed by using crop simulation model. Crop simulation models can help analyze the effect of environmental and management constraints on crop productivity. Recent studies in Iran have shown that fall- and early-sown chickpea increased the yield over late-sown because the plant used the rainfall more efficiently. The question remains whether one supplementary irrigation in the arid and semi-arid areas of Khorasan province in combination with an optimal sowing date can increase chickpea productivity. While obtaining the irrigation water requirements for potential water-limited yield might be difficult and costly, most farmers in this region are able to irrigate chickpeas twice during growing season. The present study investigated the effect of one supplementary irrigation on the yield and yield gaps in the major chickpea-growing regions in Iran.
Materials and methods
Study area and weather data
Twelve locations in Khorasan-Razavi province in northeastern Iran were studied. Long-term daily weather data for the period of 1982–2012, including solar radiation (MJ m−2 d−1), precipitation (mm), and maximum and minimum air temperature (°C) were collected for each location from its climatological station.
Crop model
The SSM-legumes model of Soltani and Sinclair (2011) was used in this study. It has been widely tested and used in Iran (Soltani and Sinclair, 2012; Amiri Deh Ahmadi et al., 2014) and India.
Yield gaps were defined as:
YG= Simulated potential yield - simulated yield at supplementary irrigation at flowering
YG= Simulated potential yield - simulated yield at supplementary irrigation at both flowering and podding.
Results and Discussions
Supplementary irrigation substantially increased grain yield at all locations. The delayed sowing dates (21 April) shortened the time from emergence to maturity substantially in response to a rise in temperature later part of growing season. One possible reason for higher yield at early sowing is the longer growing season. In early-sown crops, growth coincided with more favorable climatic conditions and increased biomass and grain yields. For instance, for early sowing at all study locations, the flowering stage did not coincide with rising temperatures at the end of season that often decreases chickpea grain yield.
Application of supplementary irrigation date did not prevent yield reduction due to delayed sowing; a delay in sowing date decreased yield at all locations. However, under delayed sowing, supplementary irrigation helped overcome severe water stress and prevent complete crop failure caused by the cessation of rainfall during the reproductive stage. A similar trend was observed for the another supplementary irrigation scenario.
the highest and lowest potential yield obtained in Taibad and Mashhad with an average yield of 2736 and 2306 Kg ha-1 respectively. Furthermore, in all of irrigation levels and the optimal sowing dates, the highest and lowest yield was observed in Quchan and Taibad respectively. The highest and lowest yield gap between the potential yield and irrigation levels were observed in Taibad and Mashhad respectively. The results indicated that supplementary irrigation at flowering and podding both reduced yield gap which is strategy in semi-arid areas with low precipitation.
Conclusions
The results show that the SSM-legume model can be used for evaluation of production limitation at the study area efficiently. The model makes it possible to explore better irrigation management resulting in maximum grain yield Supplementary irrigation is found as the best field management strategy to combat the water stress if there is access for irrigation water. Limited amounts of supplementary irrigation (irrigation at flowering and pod-filling) coupled with early sowing have the potential to play a major role in boosting and stabilizing the productivity and decreasing yield gap of chickpea.
Keywords: Actual yield, Modeling, Rainfed, Supplemental irrigation.
Amiri Deh Ahmadi, S.R., M. Parsa, M. Bannayan., and M. Nassiri Mahallati. 2015. Yield gap analysis of chickpea under semi-arid conditions: A simulation study. Journal of Agroecology 7(1): 84-98. (In Persian with English Summary)
Soltani, A., and Sinclair, T.R. 2012. Optimizing chickpea phenology to available water under current and future climates. European Journal of Agronomy 38: 22-31.
Soltani, A., Sinclair, T.R., 2011. A simple model for chickpea development, growth and yield. Field Crops Research 124, 252–260.
Safflower (Carthamus tinctorius) is one of the most important agronomic and medicinal crops in Iran, that it’s potential yield and limitations can be determined using a simple model and long-term meteorological data. This study was performed to yield prediction and statistical modeling of Safflower based on meteorological indicators and climatic parameters. Phenology, dry matter production and distribution and soil-water balance sub models should be studied in order to growth stages and yield prediction in agricultural crops. Parameters related to each sub model were estimated using data reported on different sowing dates during the years 2002-2015 in Isfahan region and the data reported by other researchers in other regions. Growth and yield changes were calculated by phenology, dry matter production and distribution using meteorological data (minimum and maximum temperatures, radiation and rainfall) from Isfahan region, and the safflower crop yield at the end of growing season was predicted. One of the model evaluation criteria is comparison of coefficient of linear regression between observed and predicted yield (a= 0.46 ± 0.073, b= 1.49 ± 0.18) with coefficient of line 1:1. In the field experiments the limit for Coefficient of variation (CV) is 20 to 25. Accuracy of the model was high, regarding to the coefficient of variation of predicted and observed grain yield (CV=8.89). R2 of grain yield was 0.75, which is indicating that predicted data are 70 percent likely match with observed data. Variation range for observed data was 1.2 to 4.61 tones per hectar and the mean was 2.9 tones and for the predicted data it was 1.94 to 3.62 tones per hectar and the mean was 2.78 Tones per hectar. In all cases, simulated yield compliance with observed yield. Hence, given the ability of the model to simulate the phonological stages of safflower, it can be used as a suitable tool for planning and better management of safflower fields in Isfahan
Australian agriculture is dominated by rainfed cropping in environments where evaporative demand greatly exceeds annual rainfall. In this paper we review field measurements of crop transpiration and bare soil evaporation under rainfed grain crops, and crop transpiration efficiencies. Crop transpiration is typically calculated from the difference between evapotranspiration and bare soil evaporation, however, while the former is readily measured, the latter is difficult to obtain. For wheat we found only 19 studies which measured the critical water balance parameters of bare soil evaporation and crop transpiration in Australia, and very many fewer for other crops. From the studies reported for wheat, on average 38% of evapotranspiration was lost to direct soil evaporation. Data for other crops are insufficient to ascertain whether they are similar or different to wheat in terms of the relative contributions of Es and T to the water balance. Although it may have occurred in practice, we can find no field measurements of the crop water balance to demonstrate an increase in crop transpiration at the expense of bare soil evaporation as a function of improvements in agronomic practices in recent decades. Although it is thought that crop transpiration efficiencies are primarily a function of vapour pressure deficit, transpiration efficiencies reported in the literature vary considerably within crops, even after accounting for vapour pressure deficit. We conclude that more reliable estimates of crop transpiration efficiency would be highly valuable for calculating seasonal transpiration of field grown crops from shoot biomass measurement, and provide an fruitful avenue for exploring water use efficiency of grain crops.
To prediction growth and yield of crops should be studied phonological sub models, production and distribution of dry matter and soil water balance. This study was conducted to predict the yield of wheat on weather conditions in Hamedan. model parameters for each sub model were estimated by using data from different sowing date in the years from 1982 to 2002 in Hamadan and data from other researchers in other parts. According to the province meteorological data (minimum temperature, maximum temperature, irradiance and rainfall) and by using the following sub models of phenology, production and distribution of dry matter was calculated changes of growth and yield. And yield of wheat crop yield was predicted at the end of the growing season. Variations of grain yield was observed between 4.08 to 8.01 ton per hectare and the average data was 6.09 tons and for prediction data Range of yield Variations was between 4.08 to 7.59 ton per hectare and that average was 5.53 ton per hectare that all of the them Simulated yield had a good correspond with the observed yield. Thus, according to the ability to appropriate the model to simulate phenological stages of wheat, Could be use it as a convenient tool for better management planning and wheat fields as well as a decision support system used in Hamedan.
Keywords: Meteorological data, wheat, Management, Grain yield, Hamedan.
The concept of a linear increase in harvest index, dHI/dt, has proven very useful for crop simulation modeling. The effect of high temperature on the response of dHI/dt of pods and seeds of peanut (Arachis hypogaea L.) has not been described. The objectives of this work were to determine (i) whether dHI/dt was linear at high temperature, (ii) whether high temperature affected dHI/dt and/or the timing of the linear phase of increase in HI, and (iii) whether there was genotypic variation in the response of dHI/dt to high temperature. Four peanut genotypes varying in heat tolerance were grown in pots at either 28/22 or 38/22°C from 21 to 90 d after planting (DAP). Plants were harvested on 10 occasions starting 27 DAP and total dry matter accumulation and partitioning measured. High temperature reduced total dry weight by 20 to 35%, seed HI by 0 to 65%, and seed dry weight by 23 to 78%. At 28/22°C, dHI/dt for pods and seeds was linear and varied from 0.0058 to 0.0109 d⁻¹ At 38/22°C, dHI/dt of pods and seeds was also linear and varied from 0.0028 to 0.0089 d⁻¹ There were genotypic differences in response to temperature. High temperature had no effect on dHI/dt in moderately tolerant genotypes 796 and 47‐16. In susceptible genotypes ICGV 86016 and ICGV 87282, however, the start of pod and seed filling was delayed by 5 to 9 d and dHI/dt reduced by 20 to 65% at 38/22°C. Reductions in pod and seed dry weight at 38/22°C were therefore due to reductions in total dry matter and dHI/dt, depending on the heat tolerance of the genotype. Crop models need to account for genotypic differences in the response of timing and rate of dHI/dt to high temperature to successfully simulate yields in warmer environments.
In order to modeling of growth stages and yield of wheat according to Hamedan province meteorological data (minimum and maximum temperature, radiation and rainfall) By using the sub models of phenology, production and distribution of dry matter and leaf area changes in maize studies was conducted at the Faculty of Agriculture, University of Vali-e-Asr Rafsanjan in spring 2015. The parameters of sub model were estimated according to data from previous researches in Iran and other countries. Daily changes of phenology, harvest Index total dry matter and leaf area was calculated using the model and the yield at the end of season was predicted. One of the criteria to evaluation of a model is Comparison between coefficients of linear regression of observed and predicted yield (b=0.90±0.67 and a=0.73±0.10) and coefficients of line 1:1 (1, 0). Accuracy of the model related to coefficient of variations of predicted and observed seed yield (CV= 7.28) was very high so that in field experiments coefficient of variations limit is 20 to 25. R2 quantity of seed yield was 0.81; showing that the probability for coordination of predicted and observed data is 81 percent. The Root mean square error is the other statistics which is used to evaluation of model accuracy. The Root mean square error of seed yield was 0.43, which is evidence of accuracy of model for yield prediction. domain variation for observed and predicted data were 4.08-8.01 tones and 4.08-7.59 tons per hectare respectively and the means were 6.09 and 5.53 tones per hectare respectively.
Climate change has been projected to significantly affect agricultural productivity and hence food availability in the coming decades. The uncertainty associated with projecting climate change impacts is a barrier to agricultural adaptation. Despite uncertainty quantification becoming more prominent in impact studies, the thorough quantification of more than one uncertainty source is not commonly exercised. This work focuses on Indian groundnut and uses the General Large Area Model for annual crops (GLAM) to investigate the response of groundnut under future climate scenarios, develop a genotypic adaptation strategy, and quantify the main uncertainty sources. Results suggest that despite large uncertainty in yield projections (to which crop- and climate-related sources contribute 46 and 54 %, respectively) no-regret strategies are possible for Indian groundnut. Benefits from genotypic adaptation were robust towards the choice of climate model, crop model parameters and bias-correction methods. Groundnut breeding for 2030 climates should be oriented toward increasing maximum photosynthetic rates, total assimilate partitioned to seeds, and, where enough soil moisture is available, also maximum transpiration rates. No benefit from enhanced heat stress tolerance was observed, though this trait may become important as warming intensifies. Managing yield variability remains a challenge for groundnut, suggesting that an integral approach to crop adaptation that includes year-to-year coping strategies as well as improvements in crop management is needed across all India.
The CROPGRO model is a generic crop model based on the SOYGRO, PNUTGRO, and BEANGRO models. In these earlier crop models, many species attributes were specified within the FORTRAN code. CROPGRO has one set of FORTRAN code and all species attributes related to soybean, peanut, or drybean are input from external ‘species’ files. As before, there are also cultivar attribute files. The CROPGRO model is a new generation model in several other ways. It computes canopy photosynthesis at hourly time steps using leaf-level photosynthesis parameters and hedge-row light interception calculations. This hedgerow approach gives more realistic response to row spacing and plant density. The hourly leaf-level photosynthesis calculations allow more mechanistic response to climatic factors as well as facilitating model analysis with respect to plant physiological factors. There are several evapotranspiration options including the Priestley-Taylor and FAO-Penman. An important new feature is the inclusion of complete soilplant N balance, with N uptake and N2-fixation, as well as N deficiency effects on photosynthetic, vegetative and seed growth processes. The N2-fixation option also interacts with the modeled carbohydrate dynamics of the plant. CROPGRO has improved phenology prediction based on newly-optimized coefficients, and a more flexible approach that allows crop development during various growth phases to be differentially sensitive to temperature, photoperiod, water deficit, and N stresses. The model has improved graphics and sensitivity analysis options to evaluate management, climate, genotypic, and pest damage factors. Sensitivity of growth processes and seed yield to climatic factors (temperature, CO2, irradiance, and water supply) and cultural management (planting date and row spacing) are illustrated.
Faced with the problem of assessing the impact of farmers' nitrogen supply practices on cottonseed yield. Sodefitex is currently seeking solutions to improve the use of crop simulation models. Technical collaboration between research and development teams resulted in the design of an experimental COTONS® model release which could be run using technicians expertise on soil and plant development. Diagnoses on available data and specific Senegalese crop conditions led to propose an hybrid version, bringing together the advantages of both standard and simple models releases used for production estimates at regional level. Technical options were then used for two current variety calibrations, and validation tests were carried out using three sites and four-year experimental data. The process seems promising for the setting up of a farming advice-oriented tool.
The fourth edition of this important book covers the advances in livestock mineral nutrition, updated with more illustrations and additional material on the relationship between livestock and man. Recent developments are discussed, such as increasing the 'mineral value' of feeds by the use of additives and enhancing mineral availability through the use of organic sources of trace elements. The concept of the 'mineral footprint' of livestock production is introduced and methods of mineral feeding that lower environmental pollution are presented. Opportunities and problems in manipulating the mineral content of livestock to improve the mineral status of consumers are also addressed. The book is an essential resource for researchers and students in animal nutrition, agriculture and veterinary medicine, and a useful reference for those concerned with human nutrition and environmental protection.
Estimates of the response of crops to climate change rarely quantify the uncertainty inherent in the simulation of both climate and crops. We present a crop simulation ensemble for a location in India, perturbing the response of both crop and climate under both baseline (12 720 simulations) and doubled-CO(2) (171720 simulations) climates. Some simulations used parameter values representing genotypic adaptation to mean temperature change. Firstly, observed and simulated yields in the baseline climate were compared. Secondly, the response of yield to changes in mean temperature was examined and compared to that found in the literature. No consistent response to temperature change was found across studies. Thirdly, the relative contribution of uncertainty in crop and climate simulation to the total uncertainty in projected yield changes was examined. In simulations without genotypic adaptation, most of the uncertainty came from the climate model parameters. Comparison with the simulations with genotypic adaptation and with a previous study suggested that the relatively low crop parameter uncertainty derives from the observational constraints on the crop parameters used in this study. Fourthly, the simulations were used, together with an observed dataset and a simple analysis of crop cardinal temperatures and thermal time, to estimate the potential for adaptation using existing cultivars. The results suggest that the germplasm for complete adaptation of groundnut cultivation in western India to a doubled-CO(2) environment may not exist. In conjunction with analyses of germplasm and local management
The sustainability of the groundnut production in North of Senegal hydro-agricultural systems depends on the development of irrigation methods which guarantee yield, quality and meet best water management practices. To achieve this goal, a water balance model taking into account plant development and soil water status was developed. The proposed model expresses evapotranspiration as a function of the observed leaf area index (LAI) and simulated soil water status. Compared to classical tools, mostly based on crop coefficients, the conceptual advantage of the developed model is to take into account not only the impact of water availability on leaf development of the crop but also their incidence on transpiration. Irrigation scheduling consists of determining net irrigation levels by adjusting the fraction of transpirable soil water (FTSW). The model was first calibrated and validated using experiments carried out in 1994, 1996 and 2000, on two groundnut varieties, Fleur 11 and GH 119-20, at the experimental station of Bambey in Senegal, using sprinkler irrigation methods. The model was then applied in 2000 during the dry season to the Senegal river valley under drip irrigation conditions so as to schedule irrigation according to diverse situations of water resource availability. Evidence was made of the model capacity to devise a sustainable irrigation method which guarantees yield, quality and appropriate water management practices.
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