## Problem Description and Formulation

The problem is a classic example of a linear programming problem. The goal is to maximize the revenue of Organic Farming by determining the optimal number of acres to allocate to growing daikons and fennels, given the constraints on land, watering hours, and compost.

Let's define the decision variables:

- \(x\): the number of acres for daikons
- \(y\): the number of acres for fennels

The objective function to maximize revenue is:
\[ \text{Maximize:} \quad 300x + 250y \]

Subject to the following constraints:

1. Land constraint: \( x + y \leq 300 \)
2. Watering hours constraint: \( 0.5x + 1.5y \leq 500 \)
3. Compost constraint: \( 70x + 50y \leq 7400 \)
4. Non-negativity constraints: \( x \geq 0, y \geq 0 \)

## Gurobi Code

To solve this problem using Gurobi in Python, we will use the Gurobi Python interface. First, ensure you have Gurobi installed and a valid license.

```python
import gurobi

def organic_farming_optimization():
    # Create a new model
    model = gurobi.Model()

    # Define the decision variables
    x = model.addVar(lb=0, name="daikon_acres")
    y = model.addVar(lb=0, name="fennel_acres")

    # Objective function: Maximize revenue
    model.setObjective(300*x + 250*y, gurobi.GRB.MAXIMIZE)

    # Land constraint
    model.addConstr(x + y <= 300, name="land_constraint")

    # Watering hours constraint
    model.addConstr(0.5*x + 1.5*y <= 500, name="watering_hours_constraint")

    # Compost constraint
    model.addConstr(70*x + 50*y <= 7400, name="compost_constraint")

    # Optimize
    model.optimize()

    # Print the status
    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal Solution:")
        print(f"Daikon Acres: {x.varValue}")
        print(f"Fennel Acres: {y.varValue}")
        print(f"Max Revenue: {model.objVal}")
    else:
        print("The model is infeasible.")

organic_farming_optimization()
```

This code defines the optimization problem as described, sets up the model with Gurobi, and solves it. The solution will provide the number of acres for daikons and fennels that maximizes revenue under the given constraints.