Introduction

Miracle Farm Blueprint

Organic Farming Manual

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Managing agricultural lands to increase soil organic carbon (SOC) could help counter the rising atmospheric CO2 concentration as well as reduce soil degradation and improve crop productivity. However, soils, climate, and management practices vary over space and time, creating an almost infinite combination of factors that interact and influence how much carbon is stored in soils. Thus, quantifying soil carbon sequestration under widely varying conditions is complicated. Furthermore, SOC changes slowly over time; experiments for quantifying carbon gain under different practices must be conducted over a number of years. Due to the human and financial resources and time needed to conduct such experiments, it may not be practical to rely on this approach alone to provide needed information. Further complicating the picture is climate change. As temperature and atmospheric CO2 increase and rainfall changes, new combinations of factors will occur that have not been studied. For these reasons, models are needed to complement information gained from experiments to help understand and predict SOC and food production responses to soil, climate, and management combinations.

Biophysical models integrate crop, soil, weather, and management practice information and predict the consequent biomass and yield components as well as changes in soil nutrients and carbon (Cole et al., 1987; Moulin and Beckie,

1993; Singh et al., 1993; Probert et al., 1995; Gijsman et al., 2002a; Jones et al., 2003). By simulating responses for a number of years, it is possible to estimate potential changes in productivity and SOC. Through a series of computer experiments, using models along with local soil and climate information, one could identify cropping systems that would meet productivity and SOC sequestration goals. However, there are a number of uncertainties associated with models and their use, and if one does not adequately address these uncertainties, simulated results will be meaningless. These uncertainties are due to the fact that models are simplifications of reality, there are uncertainties in model parameters, and there are uncertainties in inputs used in computer experiments. Thus, work is needed to ensure that models can reproduce responses measured in real experiments. This may require one to estimate crop and soil parameters (e.g., Mavro-matis et al., 2001; Gijsman et al., 2002b), to adjust other relationships to adapt the model for the region in which it is to be used (e.g., du Toit et al., 1998), and possibly conduct new research to evaluate predictions. If the model accurately describes yield and SOC responses measured in real experiments in the region, one will have more confidence in its ability to predict responses under other combinations of soil, weather, and management practices.

Biophysical models may also be useful in monitoring SOC changes over time and space to fulfill carbon contract requirements. Although this is not a common use of agricultural models, methods developed in other fields of science and engineering can be applied to help quantify and verify soil carbon sequestration. Once models have been adapted for a region, they can be used to predict changes in soil C under weather conditions that occur each year and for management practices actually used at lower cost than empirical research (Bationo et al., 2003). However, model predictions are uncertain, even if inputs are accurate. Spatial variability of inputs adds to the uncertainties outlined above, which results in propagation of prediction errors over space and time. Measurements of carbon also are uncertain and costly; errors may be much larger than annual changes in SOC. Thus, by combining measurements with model predictions, more accurate estimates of SOC can be obtained (Jones et al., 2004; Koo et al., 2003; Bostick et al., 2003).

Existing models are useful tools for understanding and predicting SOC changes if they are combined with measurements and used carefully. Our objective is to demonstrate the use of biophysical models in combination with data for two different types of uses. In the first demonstration, we explore options for increasing yield and SOC in a maize farming system in Mali. West and Post (2002) found a global average C sequestration rate of 570 kg ha-1 year-1 for no-till vs. conventional tillage when they analyzed data from 67 long-term experiments from around the world. In a 10-year study in Burkina Faso, soil C increase averaged 116 and 377 kg ha-1 year-1 for treatments with low and high levels of both inorganic fertilizer and manure, respectively (Pichot et al., 1981). Lal (2000) observed annual rates of soil C increase under no-till management ranging from 363 kg ha-1 year-1 to more than 1000 (for one severely depleted soil) over a 3-year experiment aimed at restoring soil carbon in western Nigeria. Because soil C in western African soils is known to be depleted (Bationo et al., 2003), the hypothesis used to guide our study was that ridge tillage (RT) combined with manure, nitrogen fertilizer applications, and residue management will increase soil carbon by 0.20% in 10 years (about 500 kg ha-1 year-1) relative to levels under conventional tillage (CT) management. The DSSAT-CENTURY model is used to simulate annual maize growth and yield as well as changes in SOC for 10 years. But first, care is taken to adapt the model to maize cultivars, soil, management, and climate conditions of Mali using available, although limited, data. In the second demonstration, the hypothesis is that model predictions of soil carbon can be combined with in situ measurements to improve estimates of soil carbon sequestration. An ensemble Kalman filter approach is used to assimilate observations over time into a simple model to increase accuracy of SOC estimates and to improve future predictions for specific fields.

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