Simulating spatial variability of cereal yields from historical yield maps and satellite imagery

[Abstract]: The management of spatial variability of crop yields relies on the availability of affordable and accurate spatial data. Yield maps are a direct measure of the crop yields, however, costs and difficulties in collection and processing to generate yield maps results in poor availability of such data in Australia. In this study, we used historical mid-season normalised difference vegetation index (NDVI), generated from Landsat imagery over 4 years. Using linear regression model, the NDVI was compared to the actual yield map from a 257 ha paddock. The difference between actual and predicted yield showed that 77% and 93% of the paddock area had an error of <20% and <30%, respectively. The linear model obtained in the paddock was used to simulate crop yield for an adjoining paddock of 162 ha. On an average of 4 years, the difference between actual and simulated yield showed that 87% of the paddock had an error of <20%. However, this error varied from season to season. Paddock area with <20% error increased exponentially with decreasing in-crop rainfall between anthesis and crop maturity. Furthermore, the error in simulating crop yield also varied with the soil constraints. Paddock zones with high concentrations of subsoil chloride and surface soil exchangeable sodium percentage generally had higher percent of error in simulating crop yields. Satellite imagery consistently over-predicted cereal yields in areas with subsoil constraints, possibly due to chloride-induced water stress during grain filling. The simulated yield mapping methodology offers an opportunity to identify within-field spatial variability using satellite imagery as a surrogate measure of biomass. However, the ability to successfully simulate crop yields at farm scale or regional scale requires wider evaluation across different soil types and climatic conditions

Thirty years of growing cereal without P and K fertilization

Over thirty years a significant depletion of P and K in soil occured when the were not given in fertilizers. This caused a reduction in crop yield. An abundant P application exceeding the crop uptake very clearly prevented the yield reduction but did not raise the extractable P concentration in the soil. Severe K deficiency did not start to appear until 20 years of growing cereal without fertilizer K. K application compensating for the uptake by the crop did not prevent the decrease of its extractable concentration in this soil, but this decrease did not affect crop yield

IT architecture of the MARS crop yield forecasting system

The Crop Growth Monitoring System (CGMS) provides operational services and analysis tools to the Joint Research Centre of the European Commission (JRC) in the area of crop monitoring and crop yield forecast, as part the MARS Crop Yield Forecasting System

Inverse meta-modelling to estimate soil available water capacity at high spatial resolution across a farm

Geo-referenced information on crop production that is both spatially- and temporally-dense would be useful for management in precision agriculture (PA). Crop yield monitors provide spatially but not temporally dense information. Crop growth simulation modelling can provide temporal density, but traditionally fail on the spatial issue. The research described was motivated by the challenge of satisfying both the spatial and temporal data needs of PA. The methods presented depart from current crop modelling within PA by introducing meta-modelling in combination with inverse modelling to estimate site-specific soil properties. The soil properties are used to predict spatially- and temporally-dense crop yields. An inverse meta-model was derived from the agricultural production simulator (APSIM) using neural networks to estimate soil available water capacity (AWC) from available yield data. Maps of AWC with a resolution of 10 m were produced across a dryland grain farm in Australia. For certain years and fields, the estimates were useful for yield prediction with APSIM and multiple regression, whereas for others the results were disappointing. The estimates contain ‘implicit information’ about climate interactions with soil, crop and landscape that needs to be identified. Improvement of the meta-model with more AWC scenarios, more years of yield data, inclusion of additional variables and accounting for uncertainty are discussed. We concluded that it is worthwhile to pursue this approach as an efficient way of extracting soil physical information that exists within crop yield maps to create spatially- and temporally-dense dataset

Crop Yield and Price Distributional Effects on Revenue Hedging

The use of crop yield futures contracts is examined. The expectation being modeled here reflects that of an Illinois corn and soybeans producer at planting, of revenue realized at harvest. The effects of using price and crop yield contracts are measured by comparing the results of the expected distribution to the expected distribution found under five general alternatives: 1) a revenue hedge using just price futures, 2) a revenue hedge using crop yield futures, 3) an unhedged scenario where revenue is determined by realized prices and yields, 4) an unhedged scenario where revenue is determined by realized prices and yields and by participation in government support programs with deficiency payments, and 5) a no hedge scenario where revenue is determined by realized prices and yields and by participation in a proposed revenue-assurance program. We draw four major conclusions from the results. First, hedging effectiveness using the new crop yield contract depends critically on yield basis risk which presumably can be reduced considerably by covering large geographical areas. Second, crop yield futures can be used in conjunction with price futures to derive risk management benefits significantly higher than using either of the two alone. Third, hedging using price and crop yield futures has a potential to offer benefits larger than those from the simulated revenue assurance program. However, the robustness of the findings depends largely on whether yield basis risk varies significantly across regions. Finally, the qualitative results described by the above three conclusions do not change depending on whether yields are distributed according to the beta or lognormal distribution.published or submitted for publicationnot peer reviewe

Results of trials with poppy seed (Papaver somniferum) in organic and integrated production technology.

The influence of organic and integrated management practices on poppy yield, pests and disease incidence was assessed in field trials in 2009. Crop management based on mineral fertilisers application and chemosynthetic pesticides treatment significantly increased the yield of poppy compared to organic crop protection and organic management of fertilization. Integrated crop protection decreased harmfulness of pests as rate of infectious diseases observed on capsules during harvest

ESTIMATING CORN YIELD RESPONSE MODELS TO PREDICT IMPACTS OF CLIMATE CHANGE

Projections of the impacts of climate change on agriculture require flexible and accurate yield response models. Typically, estimated yield response models have used fixed calendar intervals to measure weather variables and omitted observations on solar radiation, an essential determinant of crop yield. A corn yield response model for Illinois crop reporting districts is estimated using field data. Weather variables are time to crop growth stages to allow use of the model if climate change shifts dates of the crop growing season. Solar radiation is included. Results show this model is superior to conventionally specified models in explaining yield variation in Illinois corn.Crop Production/Industries,

Effect of organic, low-input and conventional production systems on yield and diseases in winter barley

The effect of organic, low-input and conventional management practices on barley yield and disease incidence was assessed in field trials over two years. Conventional fertility management (based on mineral fertiliser applications) and conventional crop protection (based on chemosynthetic pesticides) significantly increased the yield of winter barley as compared to organic fertility and crop protection regimes. Severity of leaf blotch (Rhynchosporium secalis) was highest under organic fertility and crop protection management and was correlated inversely with yield. For mildew (Erysiphe graminis), an interaction between fertility management and crop protection was detected. Conventional crop protection reduced severity of the disease, only under conventional fertility management. Under organic fertility management, incidence of mildew was low and application of synthetic pesticides in “low input” production systems had no significant effect on disease severity